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2023
	Cai, Hongwei; Ao, Zheng; Tian, Chunhui; Wu, Zhuhao; Liu, Hongcheng; Tchieu, Jason; Gu, Mingxia; Mackie, Ken; Guo, Feng Brain organoid reservoir computing for artificial intelligence Journal Article Nature Electronics, 2023. Abstract \| Links \| BibTeX \| Tags: 3D Culture, HD-MEA, Machine Learning, MaxOne, MEA Technology, Organoids, Stimulation @article{Cai2023b, title = {Brain organoid reservoir computing for artificial intelligence}, author = {Hongwei Cai and Zheng Ao and Chunhui Tian and Zhuhao Wu and Hongcheng Liu and Jason Tchieu and Mingxia Gu and Ken Mackie and Feng Guo }, url = {https://www.nature.com/articles/s41928-023-01069-w}, doi = {10.1038/s41928-023-01069-w}, year = {2023}, date = {2023-12-11}, journal = {Nature Electronics}, abstract = {Brain-inspired computing hardware aims to emulate the structure and working principles of the brain and could be used to address current limitations in artificial intelligence technologies. However, brain-inspired silicon chips are still limited in their ability to fully mimic brain function as most examples are built on digital electronic principles. Here we report an artificial intelligence hardware approach that uses adaptive reservoir computation of biological neural networks in a brain organoid. In this approach—which is termed Brainoware—computation is performed by sending and receiving information from the brain organoid using a high-density multielectrode array. By applying spatiotemporal electrical stimulation, nonlinear dynamics and fading memory properties are achieved, as well as unsupervised learning from training data by reshaping the organoid functional connectivity. We illustrate the practical potential of this technique by using it for speech recognition and nonlinear equation prediction in a reservoir computing framework.}, keywords = {3D Culture, HD-MEA, Machine Learning, MaxOne, MEA Technology, Organoids, Stimulation}, pubstate = {published}, tppubtype = {article} } Close Brain-inspired computing hardware aims to emulate the structure and working principles of the brain and could be used to address current limitations in artificial intelligence technologies. However, brain-inspired silicon chips are still limited in their ability to fully mimic brain function as most examples are built on digital electronic principles. Here we report an artificial intelligence hardware approach that uses adaptive reservoir computation of biological neural networks in a brain organoid. In this approach—which is termed Brainoware—computation is performed by sending and receiving information from the brain organoid using a high-density multielectrode array. By applying spatiotemporal electrical stimulation, nonlinear dynamics and fading memory properties are achieved, as well as unsupervised learning from training data by reshaping the organoid functional connectivity. We illustrate the practical potential of this technique by using it for speech recognition and nonlinear equation prediction in a reservoir computing framework. Close https://www.nature.com/articles/s41928-023-01069-w doi:10.1038/s41928-023-01069-w Close
	Elliott, Matthew A T; Schweiger, Hunter E; Robbins, Ash; Vera-Choqqueccota, Samira; Ehrlich, Drew; Hernandez, Sebastian; Voitiuk, Kateryna; Geng, Jinghui; Sevetson, Jess L; Core, Cordero; Rosen, Yohei M; Teodorescu, Mircea; Wagner, Nico O; Haussler, David; Mostajo-Radji, Mohammed A Internet-Connected Cortical Organoids for Project-Based Stem Cell and Neuroscience Education Journal Article eNeuro, 2023. Abstract \| Links \| BibTeX \| Tags: 3D Culture, Activity Scan Assay, HD-MEA, IPSC, MaxOne, MEA Technology, Organoids, Spike Sorting, Stimulation @article{Elliott2023, title = {Internet-Connected Cortical Organoids for Project-Based Stem Cell and Neuroscience Education}, author = {Matthew A. T. Elliott and Hunter E. Schweiger and Ash Robbins and Samira Vera-Choqqueccota and Drew Ehrlich and Sebastian Hernandez and Kateryna Voitiuk and Jinghui Geng and Jess L. Sevetson and Cordero Core and Yohei M. Rosen and Mircea Teodorescu and Nico O. Wagner and David Haussler and Mohammed A. Mostajo-Radji}, url = {https://www.eneuro.org/lookup/doi/10.1523/ENEURO.0308-23.2023}, doi = {10.1523/ENEURO.0308-23.2023}, year = {2023}, date = {2023-11-28}, journal = {eNeuro}, abstract = {The introduction of Internet-connected technologies to the classroom has the potential to revolutionize STEM education by allowing students to perform experiments in complex models that are unattainable in traditional teaching laboratories. By connecting laboratory equipment to the cloud, we introduce students to experimentation in pluripotent stem cell (PSC)-derived cortical organoids in two different settings: using microscopy to monitor organoid growth in an introductory tissue culture course and using high-density (HD) multielectrode arrays (MEAs) to perform neuronal stimulation and recording in an advanced neuroscience mathematics course. We demonstrate that this approach develops interest in stem cell and neuroscience in the students of both courses. All together, we propose cloud technologies as an effective and scalable approach for complex project-based university training.}, keywords = {3D Culture, Activity Scan Assay, HD-MEA, IPSC, MaxOne, MEA Technology, Organoids, Spike Sorting, Stimulation}, pubstate = {published}, tppubtype = {article} } Close The introduction of Internet-connected technologies to the classroom has the potential to revolutionize STEM education by allowing students to perform experiments in complex models that are unattainable in traditional teaching laboratories. By connecting laboratory equipment to the cloud, we introduce students to experimentation in pluripotent stem cell (PSC)-derived cortical organoids in two different settings: using microscopy to monitor organoid growth in an introductory tissue culture course and using high-density (HD) multielectrode arrays (MEAs) to perform neuronal stimulation and recording in an advanced neuroscience mathematics course. We demonstrate that this approach develops interest in stem cell and neuroscience in the students of both courses. All together, we propose cloud technologies as an effective and scalable approach for complex project-based university training. Close https://www.eneuro.org/lookup/doi/10.1523/ENEURO.0308-23.2023 doi:10.1523/ENEURO.0308-23.2023 Close
	Kobayashi, Toki; Shimba, Kenta; Kotani, Kiyoshi; Jimbo, Yasuhiko Measuring and Inducing the Plasticity of Single-Neuron Scale at Multiple Points Conference ISSS Xplore, 2023. Abstract \| Links \| BibTeX \| Tags: HD-MEA, MaxOne, Primary Neuronal Cell Culture, Stimulation @conference{Kobayashi2023, title = {Measuring and Inducing the Plasticity of Single-Neuron Scale at Multiple Points}, author = {Toki Kobayashi and Kenta Shimba and Kiyoshi Kotani and Yasuhiko Jimbo}, url = {https://ieeexplore.ieee.org/abstract/document/10322076}, doi = {10.1109/BMEiCON60347.2023.10322076}, year = {2023}, date = {2023-11-22}, journal = {IEEE Xplore}, publisher = {ISSS Xplore}, abstract = {Information processing in the brain is supported by the plasticity of the connections of neurons in biological neuronal networks. There is a gap between our understanding on plasticity of individual connections and that of the neuronal networks. Here, we aimed to induce and measure plasticity at the connections of individual neurons and at the network level. We achieved inducing plasticity with a combination of single cell stimulation by optogenetics and high-resolution extracellular potentials recording by high-density microelectrode array. Spike timing plasticity in a single neuron was successfully measured. Although we also tried to induce synaptic potentiation by tetanus stimulation, no significant change was observed. In the future, we will investigate how the plasticity of individual connections changes dynamics of entire network.}, keywords = {HD-MEA, MaxOne, Primary Neuronal Cell Culture, Stimulation}, pubstate = {published}, tppubtype = {conference} } Close Information processing in the brain is supported by the plasticity of the connections of neurons in biological neuronal networks. There is a gap between our understanding on plasticity of individual connections and that of the neuronal networks. Here, we aimed to induce and measure plasticity at the connections of individual neurons and at the network level. We achieved inducing plasticity with a combination of single cell stimulation by optogenetics and high-resolution extracellular potentials recording by high-density microelectrode array. Spike timing plasticity in a single neuron was successfully measured. Although we also tried to induce synaptic potentiation by tetanus stimulation, no significant change was observed. In the future, we will investigate how the plasticity of individual connections changes dynamics of entire network. Close https://ieeexplore.ieee.org/abstract/document/10322076 doi:10.1109/BMEiCON60347.2023.10322076 Close
	Duru, Jens; Rüfenacht, Arielle; Löhle, Josephine; Pozzi, Marcello; Forró, Csaba; Ledermann, Linus; Bernardi, Aeneas; Matter, Michael; Renia, André; Simona, Benjamin; Tringides, Christina M; Bernhard, Stéphane; Ihle, Stephan J; Hengsteler, Julian; Maurer, Benedikt; Zhanga, Xinyu; Nakatsuka, Nako Driving electrochemical reactions at the microscale using CMOS microelectrode arrays Journal Article Lab on a Chip, 2023, ISSN: 1473-0189. Abstract \| Links \| BibTeX \| Tags: HD-MEA, MaxOne, Primary Neuronal Cell Culture, Stimulation @article{Duru2023c, title = {Driving electrochemical reactions at the microscale using CMOS microelectrode arrays}, author = {Jens Duru and Arielle Rüfenacht and Josephine Löhle and Marcello Pozzi and Csaba Forró and Linus Ledermann and Aeneas Bernardi and Michael Matter and André Renia and Benjamin Simona and Christina M. Tringides and Stéphane Bernhard and Stephan J. Ihle and Julian Hengsteler and Benedikt Maurer and Xinyu Zhanga and Nako Nakatsuka}, url = {https://pubs.rsc.org/en/content/articlelanding/2023/lc/d3lc00630a}, doi = {10.1039/D3LC00630A}, issn = {1473-0189}, year = {2023}, date = {2023-10-30}, journal = {Lab on a Chip}, abstract = {Precise control of pH values at electrode interfaces enables the systematic investigation of pH-dependent processes by electrochemical means. In this work, we employed high-density complementary metal-oxide-semiconductor (CMOS) microelectrode arrays (MEAs) as miniaturized systems to induce and confine electrochemical reactions in areas corresponding to the pitch of single electrodes (17.5 μm). First, we present a strategy for generating localized pH patterns on the surface of the CMOS MEA with unprecedented spatial resolution. Leveraging the versatile routing capabilities of the switch matrix beneath the CMOS MEA, we created arbitrary combinations of anodic and cathodic electrodes and hence pH patterns. Moreover, we utilized the system to produce polymeric surface patterns by additive and subtractive methods. For additive patterning, we controlled the in situ formation of polydopamine at the microelectrode surface through oxidation of free dopamine above a threshold pH > 8.5. For subtractive patterning, we removed cell-adhesive poly-L-lysine from the electrode surface and backfilled the voids with antifouling polymers. Such polymers were chosen to provide a proof-of-concept application of controlling neuronal growth via electrochemically-induced patterns on the CMOS MEA surface. Importantly, our platform is compatible with commercially available high-density MEAs and requires no custom equipment, rendering the findings generalizable and accessible.}, keywords = {HD-MEA, MaxOne, Primary Neuronal Cell Culture, Stimulation}, pubstate = {published}, tppubtype = {article} } Close Precise control of pH values at electrode interfaces enables the systematic investigation of pH-dependent processes by electrochemical means. In this work, we employed high-density complementary metal-oxide-semiconductor (CMOS) microelectrode arrays (MEAs) as miniaturized systems to induce and confine electrochemical reactions in areas corresponding to the pitch of single electrodes (17.5 μm). First, we present a strategy for generating localized pH patterns on the surface of the CMOS MEA with unprecedented spatial resolution. Leveraging the versatile routing capabilities of the switch matrix beneath the CMOS MEA, we created arbitrary combinations of anodic and cathodic electrodes and hence pH patterns. Moreover, we utilized the system to produce polymeric surface patterns by additive and subtractive methods. For additive patterning, we controlled the in situ formation of polydopamine at the microelectrode surface through oxidation of free dopamine above a threshold pH > 8.5. For subtractive patterning, we removed cell-adhesive poly-L-lysine from the electrode surface and backfilled the voids with antifouling polymers. Such polymers were chosen to provide a proof-of-concept application of controlling neuronal growth via electrochemically-induced patterns on the CMOS MEA surface. Importantly, our platform is compatible with commercially available high-density MEAs and requires no custom equipment, rendering the findings generalizable and accessible. Close https://pubs.rsc.org/en/content/articlelanding/2023/lc/d3lc00630a doi:10.1039/D3LC00630A Close
	Duru, Jens; Maurer, Benedikt; Doran, Ciara Giles; Jelitto, Robert; Küchler, Joël; Ihle, Stephan J; Ruff, Tobias; John, Robert; Genocchi, Barbara; Vörös, János Investigation of the input-output relationship of engineered neural networks using high-density microelectrode arrays Journal Article Biosensors and Bioelectronics, 2023. Abstract \| Links \| BibTeX \| Tags: 2D Neuronal Culture, HD-MEA, MaxOne, Stimulation @article{Duru2023b, title = {Investigation of the input-output relationship of engineered neural networks using high-density microelectrode arrays}, author = {Jens Duru and Benedikt Maurer and Ciara Giles Doran and Robert Jelitto and Joël Küchler and Stephan J. Ihle and Tobias Ruff and Robert John and Barbara Genocchi and János Vörös }, url = {https://www.sciencedirect.com/science/article/pii/S095656632300533X?via%3Dihub}, doi = {https://doi.org/10.1016/j.bios.2023.115591}, year = {2023}, date = {2023-08-18}, journal = {Biosensors and Bioelectronics}, abstract = {Bottom-up neuroscience utilizes small, engineered biological neural networks to study neuronal activity in systems of reduced complexity. We present a platform that establishes up to six independent networks formed by primary rat neurons on planar complementary metal–oxide–semiconductor (CMOS) microelectrode arrays (MEAs). We introduce an approach that allows repetitive stimulation and recording of network activity at any of the over 700 electrodes underlying a network. We demonstrate that the continuous application of a repetitive super-threshold stimulus yields a reproducible network answer within a 15 ms post-stimulus window. This response can be tracked with high spatiotemporal resolution across the whole extent of the network. Moreover, we show that the location of the stimulation plays a significant role in the networks' early response to the stimulus. By applying a stimulation pattern to all network-underlying electrodes in sequence, the sensitivity of the whole network to the stimulus can be visualized. We demonstrate that microchannels reduce the voltage stimulation threshold and induce the strongest network response. By varying the stimulation amplitude and frequency we reveal discrete network transition points. Finally, we introduce vector fields to follow stimulation-induced spike propagation pathways within the network. Overall we show that our defined neural networks on CMOS MEAs enable us to elicit highly reproducible activity patterns that can be precisely modulated by stimulation amplitude, stimulation frequency and the site of stimulation.}, keywords = {2D Neuronal Culture, HD-MEA, MaxOne, Stimulation}, pubstate = {published}, tppubtype = {article} } Close Bottom-up neuroscience utilizes small, engineered biological neural networks to study neuronal activity in systems of reduced complexity. We present a platform that establishes up to six independent networks formed by primary rat neurons on planar complementary metal–oxide–semiconductor (CMOS) microelectrode arrays (MEAs). We introduce an approach that allows repetitive stimulation and recording of network activity at any of the over 700 electrodes underlying a network. We demonstrate that the continuous application of a repetitive super-threshold stimulus yields a reproducible network answer within a 15 ms post-stimulus window. This response can be tracked with high spatiotemporal resolution across the whole extent of the network. Moreover, we show that the location of the stimulation plays a significant role in the networks' early response to the stimulus. By applying a stimulation pattern to all network-underlying electrodes in sequence, the sensitivity of the whole network to the stimulus can be visualized. We demonstrate that microchannels reduce the voltage stimulation threshold and induce the strongest network response. By varying the stimulation amplitude and frequency we reveal discrete network transition points. Finally, we introduce vector fields to follow stimulation-induced spike propagation pathways within the network. Overall we show that our defined neural networks on CMOS MEAs enable us to elicit highly reproducible activity patterns that can be precisely modulated by stimulation amplitude, stimulation frequency and the site of stimulation. Close https://www.sciencedirect.com/science/article/pii/S095656632300533X?via%3Dihub doi:https://doi.org/10.1016/j.bios.2023.115591 Close
	Duru, Jens; Maurer, Benedikt; Doran, Ciara Giles; Jelitto, Robert; Küchler, Joël; Ihle, Stephan J; Ruff, Tobias; John, Robert; Genocchi, Barbara; Vörös, János Investigation of the input-output relationship of engineered neural networks using high-density microelectrode arrays Journal Article SSRN, 2023. Abstract \| Links \| BibTeX \| Tags: HD-MEA, MaxOne, MEA Technology, Primary Neuronal Cell Culture, Spike Sorting, Stimulation @article{Duru2023, title = {Investigation of the input-output relationship of engineered neural networks using high-density microelectrode arrays}, author = {Jens Duru and Benedikt Maurer and Ciara Giles Doran and Robert Jelitto and Joël Küchler and Stephan J. Ihle and Tobias Ruff and Robert John and Barbara Genocchi and János Vörös}, url = {https://www.ssrn.com/abstract=4427959}, doi = {DOI: 10.2139/ssrn.4427959}, year = {2023}, date = {2023-04-24}, journal = {SSRN}, abstract = {Bottom-up neuroscience utilizes small, engineered biological neural networks to study neuronal activity in systems of reduced complexity. We present a platform that establishes up to six independent networks formed by primary rat neurons on planar complementary metal–oxide–semiconductor (CMOS) microelectrode arrays (MEAs). We introduce an approach that allows repetitive stimulation and recording of network activity at any of the over 700 electrodes underlying a network. We demonstrate that the continuous application of a repetitive super-threshold stimulus yields a reproducible network answer within a 15 ms post-stimulus window. This response can be tracked with high spatiotemporal resolution across the whole extent of the network. Moreover, we show that the location of the stimulation plays a significant role in the networks’ early response to the stimulus. By applying a stimulation pattern to all network-underlying electrodes in sequence, the sensitivity of the whole network to the stimulus can be visualized. We demonstrate that microchannels reduce the voltage stimulation threshold and induce the strongest network response. By varying the stimulation amplitude and frequency we reveal discrete network transition points. Finally, we introduce vector fields to follow stimulation-induced spike propagation pathways within the network. Overall we show that our defined neural networks on CMOS MEAs enable us to elicit highly reproducible activity patterns that can be precisely modulated by stimulation amplitude, stimulation frequency and the site of stimulation.}, keywords = {HD-MEA, MaxOne, MEA Technology, Primary Neuronal Cell Culture, Spike Sorting, Stimulation}, pubstate = {published}, tppubtype = {article} } Close Bottom-up neuroscience utilizes small, engineered biological neural networks to study neuronal activity in systems of reduced complexity. We present a platform that establishes up to six independent networks formed by primary rat neurons on planar complementary metal–oxide–semiconductor (CMOS) microelectrode arrays (MEAs). We introduce an approach that allows repetitive stimulation and recording of network activity at any of the over 700 electrodes underlying a network. We demonstrate that the continuous application of a repetitive super-threshold stimulus yields a reproducible network answer within a 15 ms post-stimulus window. This response can be tracked with high spatiotemporal resolution across the whole extent of the network. Moreover, we show that the location of the stimulation plays a significant role in the networks’ early response to the stimulus. By applying a stimulation pattern to all network-underlying electrodes in sequence, the sensitivity of the whole network to the stimulus can be visualized. We demonstrate that microchannels reduce the voltage stimulation threshold and induce the strongest network response. By varying the stimulation amplitude and frequency we reveal discrete network transition points. Finally, we introduce vector fields to follow stimulation-induced spike propagation pathways within the network. Overall we show that our defined neural networks on CMOS MEAs enable us to elicit highly reproducible activity patterns that can be precisely modulated by stimulation amplitude, stimulation frequency and the site of stimulation. Close https://www.ssrn.com/abstract=4427959 doi:DOI: 10.2139/ssrn.4427959 Close
	Cai, Hongwei; Ao, Zheng; Tian, Chunhui; Wu, Zhuhao; Liu, Hongcheng; Tchieu, Jason; Gu, Mingxia; Mackie, Ken; and Guo, Feng Brain Organoid Computing for Artificial Intelligence Journal Article bioRxiv, 2023. Abstract \| Links \| BibTeX \| Tags: HD-MEA, Machine Learning, MaxOne, MEA Technology, Modeling, Organoids, Stimulation @article{Cai2023, title = {Brain Organoid Computing for Artificial Intelligence}, author = {Hongwei Cai and Zheng Ao and Chunhui Tian and Zhuhao Wu and Hongcheng Liu and Jason Tchieu and Mingxia Gu and Ken Mackie and and Feng Guo}, url = {https://www.biorxiv.org/content/10.1101/2023.02.28.530502v1}, doi = {10.1101/2023.02.28.530502}, year = {2023}, date = {2023-03-01}, journal = {bioRxiv}, abstract = {Brain-inspired hardware emulates the structure and working principles of a biological brain and may address the hardware bottleneck for fast-growing artificial intelligence (AI). Current brain-inspired silicon chips are promising but still limit their power to fully mimic brain function for AI computing. Here, we develop Brainoware, living AI hardware that harnesses the computation power of 3D biological neural networks in a brain organoid. Brain-like 3D in vitro cultures compute by receiving and sending information via a multielectrode array. Applying spatiotemporal electrical stimulation, this approach not only exhibits nonlinear dynamics and fading memory properties but also learns from training data. Further experiments demonstrate real-world applications in solving non-linear equations. This approach may provide new insights into AI hardware. }, keywords = {HD-MEA, Machine Learning, MaxOne, MEA Technology, Modeling, Organoids, Stimulation}, pubstate = {published}, tppubtype = {article} } Close Brain-inspired hardware emulates the structure and working principles of a biological brain and may address the hardware bottleneck for fast-growing artificial intelligence (AI). Current brain-inspired silicon chips are promising but still limit their power to fully mimic brain function for AI computing. Here, we develop Brainoware, living AI hardware that harnesses the computation power of 3D biological neural networks in a brain organoid. Brain-like 3D in vitro cultures compute by receiving and sending information via a multielectrode array. Applying spatiotemporal electrical stimulation, this approach not only exhibits nonlinear dynamics and fading memory properties but also learns from training data. Further experiments demonstrate real-world applications in solving non-linear equations. This approach may provide new insights into AI hardware. Close https://www.biorxiv.org/content/10.1101/2023.02.28.530502v1 doi:10.1101/2023.02.28.530502 Close
2022
	Sawada, Haruto; Wake, Naoki; Sasabuchi, Kazuhiro; Takamatsu, Jun; Takahashi, Hirokazu; Ikeuchi, Katsushi Design strategies for controlling neuron-connected robots using reinforcement learning Journal Article arXiv, 2022. Abstract \| Links \| BibTeX \| Tags: Machine Learning, MaxOne, Stimulation @article{Sawada2022, title = {Design strategies for controlling neuron-connected robots using reinforcement learning}, author = {Haruto Sawada and Naoki Wake and Kazuhiro Sasabuchi and Jun Takamatsu and Hirokazu Takahashi and Katsushi Ikeuchi}, url = {https://arxiv.org/abs/2203.15290}, doi = {https://doi.org/10.48550/arXiv.2203.15290}, year = {2022}, date = {2022-03-29}, journal = {arXiv}, abstract = {Despite the growing interest in robot control utilizing the computation of biological neurons, context-dependent behavior by neuron-connected robots remains a challenge. Context-dependent behavior here is defined as behavior that is not the result of a simple sensory-motor coupling, but rather based on an understanding of the task goal. This paper proposes design principles for training neuron-connected robots based on task goals to achieve context-dependent behavior. First, we employ deep reinforcement learning (RL) to enable training that accounts for goal achievements. Second, we propose a neuron simulator as a probability distribution based on recorded neural data, aiming to represent physiologically valid neural dynamics while avoiding complex modeling with high computational costs. Furthermore, we propose to update the simulators during the training to bridge the gap between the simulation and the real settings. The experiments showed that the robot gradually learned context-dependent behaviors in pole balancing and robot navigation tasks. Moreover, the learned policies were valid for neural simulators based on novel neural data, and the task performance increased by updating the simulators during training. These results suggest the effectiveness of the proposed design principle for the context-dependent behavior of neuron-connected robots.}, keywords = {Machine Learning, MaxOne, Stimulation}, pubstate = {published}, tppubtype = {article} } Close Despite the growing interest in robot control utilizing the computation of biological neurons, context-dependent behavior by neuron-connected robots remains a challenge. Context-dependent behavior here is defined as behavior that is not the result of a simple sensory-motor coupling, but rather based on an understanding of the task goal. This paper proposes design principles for training neuron-connected robots based on task goals to achieve context-dependent behavior. First, we employ deep reinforcement learning (RL) to enable training that accounts for goal achievements. Second, we propose a neuron simulator as a probability distribution based on recorded neural data, aiming to represent physiologically valid neural dynamics while avoiding complex modeling with high computational costs. Furthermore, we propose to update the simulators during the training to bridge the gap between the simulation and the real settings. The experiments showed that the robot gradually learned context-dependent behaviors in pole balancing and robot navigation tasks. Moreover, the learned policies were valid for neural simulators based on novel neural data, and the task performance increased by updating the simulators during training. These results suggest the effectiveness of the proposed design principle for the context-dependent behavior of neuron-connected robots. Close https://arxiv.org/abs/2203.15290 doi:https://doi.org/10.48550/arXiv.2203.15290 Close
2021
	Kubota, Tomoyuki; Takahashi, Hirokazu; and Nakajima, Kohei Unifying framework for information processing in stochastically driven dynamical systems Journal Article Physical Review Research, 2021. Abstract \| Links \| BibTeX \| Tags: 2D Neuronal Culture, HD-MEA, MaxOne, Primary Neuronal Cell Culture, Stimulation @article{Kubota2023, title = {Unifying framework for information processing in stochastically driven dynamical systems}, author = {Tomoyuki Kubota and Hirokazu Takahashi and and Kohei Nakajima}, url = {https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.3.043135}, doi = {https://doi.org/10.1103/PhysRevResearch.3.043135}, year = {2021}, date = {2021-11-23}, journal = {Physical Review Research}, abstract = {A dynamical system is an information processing apparatus that encodes input streams from the external environment to its state and processes them through state transitions. The information processing capacity (IPC) is an excellent tool that comprehensively evaluates these processed inputs, providing details of unknown information processing in black box systems; however, this measure can be applied only to time-invariant systems. This paper extends the applicable range to time-variant systems and further reveals that the IPC is equivalent to coefficients of polynomial chaos (PC) expansion in more general dynamical systems. To achieve this objective, we tackle three issues. First, we establish a connection between the IPC for time-invariant systems and PC expansion, which is a type of polynomial expansion using orthogonal functions of input history as bases. We prove that the IPC corresponds to the squared norm of the coefficient vector of the basis in the PC expansion. Second, we show that an input following an arbitrary distribution can be used for the IPC, removing previous restrictions to specific input distributions. Third, we extend the conventional orthogonal bases to functions of both time and input history and propose the IPC for time-variant systems. To show the significance of our approach, we demonstrate that our measure can reveal information representations in not only machine learning networks but also a real, cultured neural network. Our generalized measure paves the way for unveiling the information processing capabilities of a wide variety of physical dynamics which have been left behind in nature.}, keywords = {2D Neuronal Culture, HD-MEA, MaxOne, Primary Neuronal Cell Culture, Stimulation}, pubstate = {published}, tppubtype = {article} } Close A dynamical system is an information processing apparatus that encodes input streams from the external environment to its state and processes them through state transitions. The information processing capacity (IPC) is an excellent tool that comprehensively evaluates these processed inputs, providing details of unknown information processing in black box systems; however, this measure can be applied only to time-invariant systems. This paper extends the applicable range to time-variant systems and further reveals that the IPC is equivalent to coefficients of polynomial chaos (PC) expansion in more general dynamical systems. To achieve this objective, we tackle three issues. First, we establish a connection between the IPC for time-invariant systems and PC expansion, which is a type of polynomial expansion using orthogonal functions of input history as bases. We prove that the IPC corresponds to the squared norm of the coefficient vector of the basis in the PC expansion. Second, we show that an input following an arbitrary distribution can be used for the IPC, removing previous restrictions to specific input distributions. Third, we extend the conventional orthogonal bases to functions of both time and input history and propose the IPC for time-variant systems. To show the significance of our approach, we demonstrate that our measure can reveal information representations in not only machine learning networks but also a real, cultured neural network. Our generalized measure paves the way for unveiling the information processing capabilities of a wide variety of physical dynamics which have been left behind in nature. Close https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.3.043135 doi:https://doi.org/10.1103/PhysRevResearch.3.043135 Close
2019
	Ronchi, Silvia; Fiscella, Michele; Marchetti, Camilla; Viswam, Vijay; Muller, Jan; Frey, Urs; Hierlemann, Andreas Single-Cell Electrical Stimulation Using CMOS-Based High-Density Microelectrode Arrays Journal Article Frontiers in Neuroscience, 13 , 2019, ISSN: 1662-453X . Abstract \| Links \| BibTeX \| Tags: ETH-CMOS-MEA, Stimulation @article{Ronchi2019, title = {Single-Cell Electrical Stimulation Using CMOS-Based High-Density Microelectrode Arrays}, author = {Silvia Ronchi and Michele Fiscella and Camilla Marchetti and Vijay Viswam and Jan Muller and Urs Frey and Andreas Hierlemann}, url = {https://www.frontiersin.org/article/10.3389/fnins.2019.00208 }, doi = {10.3389/fnins.2019.00208 }, issn = {1662-453X }, year = {2019}, date = {2019-03-13}, journal = {Frontiers in Neuroscience}, volume = {13}, abstract = {Non-invasive electrical stimulation can be used to study and control neural activity in the brain or to alleviate somatosensory dysfunctions. One intriguing prospect is to precisely stimulate individual targeted neurons. Here, we investigated single-neuron current and voltage stimulation in vitro using high-density microelectrode arrays featuring 26’400 bidirectional electrodes at a pitch of 17.5 µm and an electrode area of 5 × 9 µm². We determined optimal waveforms, amplitudes and durations for both stimulation modes. Owing to the high spatial resolution of our arrays and the close proximity of the electrodes to the respective neurons, we were able to stimulate the axon initial segments (AIS) with charges of less than 2 picoCoulombs. This resulted in minimal artifact production and reliable readout of stimulation efficiency directly at the soma of the stimulated cell. Stimulation signals as low as 70 mV or 100 nA,with pulse durations as short as 18 µs, yielded measurable action potential initiation and propagation. We found that the required stimulation signal amplitudes decreased with cell growth and development and that stimulation efficiency did not improve at higher electric fields generated by simultaneous multi-electrode stimulation.}, keywords = {ETH-CMOS-MEA, Stimulation}, pubstate = {published}, tppubtype = {article} } Close Non-invasive electrical stimulation can be used to study and control neural activity in the brain or to alleviate somatosensory dysfunctions. One intriguing prospect is to precisely stimulate individual targeted neurons. Here, we investigated single-neuron current and voltage stimulation in vitro using high-density microelectrode arrays featuring 26’400 bidirectional electrodes at a pitch of 17.5 µm and an electrode area of 5 × 9 µm². We determined optimal waveforms, amplitudes and durations for both stimulation modes. Owing to the high spatial resolution of our arrays and the close proximity of the electrodes to the respective neurons, we were able to stimulate the axon initial segments (AIS) with charges of less than 2 picoCoulombs. This resulted in minimal artifact production and reliable readout of stimulation efficiency directly at the soma of the stimulated cell. Stimulation signals as low as 70 mV or 100 nA,with pulse durations as short as 18 µs, yielded measurable action potential initiation and propagation. We found that the required stimulation signal amplitudes decreased with cell growth and development and that stimulation efficiency did not improve at higher electric fields generated by simultaneous multi-electrode stimulation. Close https://www.frontiersin.org/article/10.3389/fnins.2019.00208 doi:10.3389/fnins.2019.00208 Close
2018
	Ronchi, Silvia; Fiscella, Michele; Marchetti, Camilla; Viswam, Vijay; Müller, Jan; Frey, Urs; Hierlemann, Andreas Single-neuron sub-cellular-resolution electrical stimulation with high-density microelectrode arrays Conference Contribution 174.05 , Society for Neuroscience (SfN) Meeting San Diego, CA, USA, 2018. Abstract \| Links \| BibTeX \| Tags: ETH-CMOS-MEA, Stimulation @conference{Ronchi2018, title = {Single-neuron sub-cellular-resolution electrical stimulation with high-density microelectrode arrays}, author = {Silvia Ronchi and Michele Fiscella and Camilla Marchetti and Vijay Viswam and Jan Müller and Urs Frey and Andreas Hierlemann}, url = {https://abstractsonline.com/pp8/#!/4649/presentation/24382}, year = {2018}, date = {2018-11-04}, volume = {Contribution 174.05}, address = {San Diego, CA, USA}, organization = {Society for Neuroscience (SfN) Meeting}, abstract = {Non-invasive electrical stimulation is a consolidated technique to study and control neural activity in the brain and peripheral nervous system. It is used, e.g., for controlling Parkinson's disease or to induce sensation in paralyzed patients (Armenta Salas et al., 2018), as well as for attempts to restore vision (Fan et al., 2018; Grosberg et al., 2017) and hearing (Wilson & Dorman, 2008). A common requirement in electrical stimulation is the precise and controlled stimulation of individual targeted neurons. For achieving this purpose, it is necessary that electrodes can stimulate and record extracellular signals at sub-cellular resolution. Furthermore, it is important to design efficient stimulation pulses to be delivered to the neurons. In the present work we used an CMOS-based high-density microelectrode array (HD-MEA) (Ballini et al., 2014), featuring 26’400 bidirectional electrodes with a pitch of 17.5 µm, which was designed for in-vitro applications. This high-resolution was used to test electrical stimulation parameters in vitro, which then could potentially be adapted to elicit single-cell action potentials in vivo. In this work we used different stimulation parameters, such as waveforms, amplitudes and durations (Grosberg et al., 2017; Wagenaar, Pine, & Potter, 2004), using 5x9 µm² electrodes to target sub-cellular structures in single neurons. E-18 Wistar rat cortical neurons were stimulated at days-in-vitro 10, 15, 20 and 25 using randomized voltage and current stimulation modalities. Axon initial segments of individual neurons (Radivojevic et al., 2016) were targeted for stimulation, enabled by the HD-MEA device. We found that voltage biphasic anodic-cathodic waveforms were less efficient than biphasic cathodic-anodic waveforms in eliciting action potentials in a single neuron. Moreover, it was possible to detect action potentials directly at the cell soma, which ensured a reliable confirmation of successful neuron stimulation. Finally, HD-MEA technology enabled to elicit action potentials in single-neurons embedded in high-density cell cultures. The obtained results can be used to optimize in vivo single-cell targeting for stimulation and read-out.}, keywords = {ETH-CMOS-MEA, Stimulation}, pubstate = {published}, tppubtype = {conference} } Close Non-invasive electrical stimulation is a consolidated technique to study and control neural activity in the brain and peripheral nervous system. It is used, e.g., for controlling Parkinson's disease or to induce sensation in paralyzed patients (Armenta Salas et al., 2018), as well as for attempts to restore vision (Fan et al., 2018; Grosberg et al., 2017) and hearing (Wilson & Dorman, 2008). A common requirement in electrical stimulation is the precise and controlled stimulation of individual targeted neurons. For achieving this purpose, it is necessary that electrodes can stimulate and record extracellular signals at sub-cellular resolution. Furthermore, it is important to design efficient stimulation pulses to be delivered to the neurons. In the present work we used an CMOS-based high-density microelectrode array (HD-MEA) (Ballini et al., 2014), featuring 26’400 bidirectional electrodes with a pitch of 17.5 µm, which was designed for in-vitro applications. This high-resolution was used to test electrical stimulation parameters in vitro, which then could potentially be adapted to elicit single-cell action potentials in vivo. In this work we used different stimulation parameters, such as waveforms, amplitudes and durations (Grosberg et al., 2017; Wagenaar, Pine, & Potter, 2004), using 5x9 µm² electrodes to target sub-cellular structures in single neurons. E-18 Wistar rat cortical neurons were stimulated at days-in-vitro 10, 15, 20 and 25 using randomized voltage and current stimulation modalities. Axon initial segments of individual neurons (Radivojevic et al., 2016) were targeted for stimulation, enabled by the HD-MEA device. We found that voltage biphasic anodic-cathodic waveforms were less efficient than biphasic cathodic-anodic waveforms in eliciting action potentials in a single neuron. Moreover, it was possible to detect action potentials directly at the cell soma, which ensured a reliable confirmation of successful neuron stimulation. Finally, HD-MEA technology enabled to elicit action potentials in single-neurons embedded in high-density cell cultures. The obtained results can be used to optimize in vivo single-cell targeting for stimulation and read-out. Close https://abstractsonline.com/pp8/#!/4649/presentation/24382 Close
	Ronchi, Silvia; Fiscella, Michele; Muller, Jan; Viswam, Vijay; Frey, Urs; Hierlemann, Andreas Single-cell electrical stimulation with CMOS-based high-density microelectrode arrays Conference 11th International Meeting on Substrate Integrated Microelectrode Arrays (MEA Meeting) Reutlingen, Germany, 2018. Abstract \| Links \| BibTeX \| Tags: ETH-CMOS-MEA, Stimulation @conference{Ronchi2018b, title = {Single-cell electrical stimulation with CMOS-based high-density microelectrode arrays}, author = {Silvia Ronchi and Michele Fiscella and Jan Muller and Vijay Viswam and Urs Frey and Andreas Hierlemann}, url = {https://www.frontiersin.org/10.3389/conf.fncel.2018.38.00086/event_abstract}, doi = {10.3389/conf.fncel.2018.38.00086}, year = {2018}, date = {2018-07-04}, address = {Reutlingen, Germany}, organization = {11th International Meeting on Substrate Integrated Microelectrode Arrays (MEA Meeting)}, abstract = {The main goal of this work was to explore electrical stimulation parameters that reproducibly and precisely elicit action potentials in single neurons (Wagenaar et al. 2004). We compared voltage and current modalities’ and their efficacy in activating single neurons; we also studied the related stimulation artifacts. For our studies, we used a CMOS-based MEA featuring 26400 electrodes at 17.5 µm pitch (Ballini et al. 2014). }, keywords = {ETH-CMOS-MEA, Stimulation}, pubstate = {published}, tppubtype = {conference} } Close The main goal of this work was to explore electrical stimulation parameters that reproducibly and precisely elicit action potentials in single neurons (Wagenaar et al. 2004). We compared voltage and current modalities’ and their efficacy in activating single neurons; we also studied the related stimulation artifacts. For our studies, we used a CMOS-based MEA featuring 26400 electrodes at 17.5 µm pitch (Ballini et al. 2014). Close https://www.frontiersin.org/10.3389/conf.fncel.2018.38.00086/event_abstract doi:10.3389/conf.fncel.2018.38.00086 Close
	Yuan, Xinyue; Hierlemann, Andreas; Frey, Urs Dual-mode Microelectrode Array with 20k-electrodes and High SNR for High-Throughput Extracellular Recording and Stimulation Conference 11th International Meeting on Substrate Integrated Microelectrode Arrays (MEA Meeting) Reutlingen, Germany, 2018. Abstract \| Links \| BibTeX \| Tags: ETH-CMOS-MEA, Stimulation @conference{Yuan2018, title = {Dual-mode Microelectrode Array with 20k-electrodes and High SNR for High-Throughput Extracellular Recording and Stimulation}, author = {Xinyue Yuan and Andreas Hierlemann and Urs Frey}, url = {https://https://www.frontiersin.org/Community/AbstractDetails.aspx?ABS_DOI=10.3389/conf.fncel.2018.38.00088&eid=5473&sname=MEA_Meeting_2018_%7C_11th_International_Meeting_on_Substrate_Integrated_Microelectrode_Arrays}, doi = {10.3389/conf.fncel.2018.38.00088}, year = {2018}, date = {2018-07-04}, address = {Reutlingen, Germany}, organization = {11th International Meeting on Substrate Integrated Microelectrode Arrays (MEA Meeting)}, abstract = {Recording and analysis of neuronal signals can provide much insight into how neurons process information and communicate with each other. Recent advancements of microelectrode-array (MEA) technology provide unprecedented means to study neuronal signals and network behavior in in vitro and in vivo applications [1], [2]. The trade-off between noise performance, power consumption and electrode density, however, remains a major challenge in MEA design. To balance this tradeoff, we designed a Dual-mode (DM) MEA that combines two major types of readout schemes, i.e., the active-pixel-sensor (APS) and switch-matrix (SM) schemes, in order to achieve high electrode density and high signal-to-noise ratio (SNR) at the same time. Based on a previous prototype [3], the new DM-MEA has shown to be a useful tool for in-vitro neuroscience studies, especially for network studies}, keywords = {ETH-CMOS-MEA, Stimulation}, pubstate = {published}, tppubtype = {conference} } Close Recording and analysis of neuronal signals can provide much insight into how neurons process information and communicate with each other. Recent advancements of microelectrode-array (MEA) technology provide unprecedented means to study neuronal signals and network behavior in in vitro and in vivo applications [1], [2]. The trade-off between noise performance, power consumption and electrode density, however, remains a major challenge in MEA design. To balance this tradeoff, we designed a Dual-mode (DM) MEA that combines two major types of readout schemes, i.e., the active-pixel-sensor (APS) and switch-matrix (SM) schemes, in order to achieve high electrode density and high signal-to-noise ratio (SNR) at the same time. Based on a previous prototype [3], the new DM-MEA has shown to be a useful tool for in-vitro neuroscience studies, especially for network studies Close https://https://www.frontiersin.org/Community/AbstractDetails.aspx?ABS_DOI=10.33[...] doi:10.3389/conf.fncel.2018.38.00088 Close
2017
	Radivojevic, Milos; Franke, Felix; Altermatt, Michael; Müller, Jan; Hierlemann, Andreas; Bakkum, Douglas J Tracking individual action potentials throughout mammalian axonal arbors Journal Article eLife, 6 , pp. 1-23, 2017, ISSN: 2050-084X. Abstract \| Links \| BibTeX \| Tags: Data Analysis, ETH-CMOS-MEA, Neuronal Networks, Stimulation @article{Radivojevic2017, title = {Tracking individual action potentials throughout mammalian axonal arbors}, author = {Milos Radivojevic and Felix Franke and Michael Altermatt and Jan Müller and Andreas Hierlemann and Douglas J Bakkum}, url = {https://elifesciences.org/articles/30198}, doi = {10.7554/eLife.30198}, issn = {2050-084X}, year = {2017}, date = {2017-10-09}, journal = {eLife}, volume = {6}, pages = {1-23}, abstract = {Axons are neuronal processes specialized for conduction of action potentials (APs). The timing and temporal precision of APs when they reach each of the synapses are fundamentally important for information processing in the brain. Due to small diameters of axons, direct recording of single AP transmission is challenging. Consequently, most knowledge about axonal conductance derives from modeling studies or indirect measurements. We demonstrate a method to noninvasively and directly record individual APs propagating along millimeter-length axonal arbors in cortical cultures with hundreds of microelectrodes at microsecond temporal resolution. We find that cortical axons conduct single APs with high temporal precision (~100 µs arrival time jitter per mm length) and reliability: in more than 8,000,000 recorded APs, we did not observe any conduction or branch-point failures. Upon high-frequency stimulation at 100 Hz, successive became slower, and their arrival time precision decreased by 20% and 12% for the 100th AP, respectively.}, keywords = {Data Analysis, ETH-CMOS-MEA, Neuronal Networks, Stimulation}, pubstate = {published}, tppubtype = {article} } Close Axons are neuronal processes specialized for conduction of action potentials (APs). The timing and temporal precision of APs when they reach each of the synapses are fundamentally important for information processing in the brain. Due to small diameters of axons, direct recording of single AP transmission is challenging. Consequently, most knowledge about axonal conductance derives from modeling studies or indirect measurements. We demonstrate a method to noninvasively and directly record individual APs propagating along millimeter-length axonal arbors in cortical cultures with hundreds of microelectrodes at microsecond temporal resolution. We find that cortical axons conduct single APs with high temporal precision (~100 µs arrival time jitter per mm length) and reliability: in more than 8,000,000 recorded APs, we did not observe any conduction or branch-point failures. Upon high-frequency stimulation at 100 Hz, successive became slower, and their arrival time precision decreased by 20% and 12% for the 100th AP, respectively. Close https://elifesciences.org/articles/30198 doi:10.7554/eLife.30198 Close
2016
	Radivojevic, Milos; Jäckel, David; Altermatt, Michael; Müller, Jan; Viswam, Vijay; Hierlemann, Andreas; Bakkum, Douglas J Electrical Identification and Selective Microstimulation of Neuronal Compartments Based on Features of Extracellular Action Potentials Journal Article Scientific Reports, 6 (1), pp. 1-20, 2016, ISSN: 2045-2322. Abstract \| Links \| BibTeX \| Tags: ETH-CMOS-MEA, Neuronal Networks, Stimulation @article{Radivojevic2016, title = {Electrical Identification and Selective Microstimulation of Neuronal Compartments Based on Features of Extracellular Action Potentials}, author = {Milos Radivojevic and David Jäckel and Michael Altermatt and Jan Müller and Vijay Viswam and Andreas Hierlemann and Douglas J Bakkum}, url = {http://www.nature.com/articles/srep31332}, doi = {10.1038/srep31332}, issn = {2045-2322}, year = {2016}, date = {2016-08-11}, journal = {Scientific Reports}, volume = {6}, number = {1}, pages = {1-20}, abstract = {A detailed, high-spatiotemporal-resolution characterization of neuronal responses to local electrical fields and the capability of precise extracellular microstimulation of selected neurons are pivotal for studying and manipulating neuronal activity and circuits in networks and for developing neural prosthetics. Here, we studied cultured neocortical neurons by using high-density microelectrode arrays and optical imaging, complemented by the patch-clamp technique, and with the aim to correlate morphological and electrical features of neuronal compartments with their responsiveness to extracellular stimulation. We developed strategies to electrically identify any neuron in the network, while subcellular spatial resolution recording of extracellular action potential (AP) traces enabled their assignment to the axon initial segment (AIS), axonal arbor and proximal somatodendritic compartments. Stimulation at the AIS required low voltages and provided immediate, selective and reliable neuronal activation, whereas stimulation at the soma required high voltages and produced delayed and unreliable responses. Subthreshold stimulation at the soma depolarized the somatic membrane potential without eliciting APs.}, keywords = {ETH-CMOS-MEA, Neuronal Networks, Stimulation}, pubstate = {published}, tppubtype = {article} } Close A detailed, high-spatiotemporal-resolution characterization of neuronal responses to local electrical fields and the capability of precise extracellular microstimulation of selected neurons are pivotal for studying and manipulating neuronal activity and circuits in networks and for developing neural prosthetics. Here, we studied cultured neocortical neurons by using high-density microelectrode arrays and optical imaging, complemented by the patch-clamp technique, and with the aim to correlate morphological and electrical features of neuronal compartments with their responsiveness to extracellular stimulation. We developed strategies to electrically identify any neuron in the network, while subcellular spatial resolution recording of extracellular action potential (AP) traces enabled their assignment to the axon initial segment (AIS), axonal arbor and proximal somatodendritic compartments. Stimulation at the AIS required low voltages and provided immediate, selective and reliable neuronal activation, whereas stimulation at the soma required high voltages and produced delayed and unreliable responses. Subthreshold stimulation at the soma depolarized the somatic membrane potential without eliciting APs. Close http://www.nature.com/articles/srep31332 doi:10.1038/srep31332 Close
2013
	Müller, Jan; Bakkum, Douglas J; Hierlemann, Andreas Sub-millisecond closed-loop feedback stimulation between arbitrary sets of individual neurons. Journal Article Frontiers in Neural Circuits, 6 , pp. 121, 2013, ISSN: 1662-5110. Abstract \| Links \| BibTeX \| Tags: ETH-CMOS-MEA, MEA Technology, Neuronal Networks, Stimulation @article{Muller2012, title = {Sub-millisecond closed-loop feedback stimulation between arbitrary sets of individual neurons.}, author = {Jan Müller and Douglas J Bakkum and Andreas Hierlemann}, url = {https://www.frontiersin.org/articles/10.3389/fncir.2012.00121/full}, doi = {10.3389/fncir.2012.00121}, issn = {1662-5110}, year = {2013}, date = {2013-01-10}, journal = {Frontiers in Neural Circuits}, volume = {6}, pages = {121}, abstract = {We present a system to artificially correlate the spike timing between sets of arbitrary neurons that were interfaced to a complementary metal-oxide-semiconductor (CMOS) high-density microelectrode array (MEA). The system features a novel reprogrammable and flexible event engine unit to detect arbitrary spatio-temporal patterns of recorded action potentials and is capable of delivering sub-millisecond closed-loop feedback of electrical stimulation upon trigger events in real-time. The relative timing between action potentials of individual neurons as well as the temporal pattern among multiple neurons, or neuronal assemblies, is considered an important factor governing memory and learning in the brain. Artificially changing timings between arbitrary sets of spiking neurons with our system could provide a "knob" to tune information processing in the network.}, keywords = {ETH-CMOS-MEA, MEA Technology, Neuronal Networks, Stimulation}, pubstate = {published}, tppubtype = {article} } Close We present a system to artificially correlate the spike timing between sets of arbitrary neurons that were interfaced to a complementary metal-oxide-semiconductor (CMOS) high-density microelectrode array (MEA). The system features a novel reprogrammable and flexible event engine unit to detect arbitrary spatio-temporal patterns of recorded action potentials and is capable of delivering sub-millisecond closed-loop feedback of electrical stimulation upon trigger events in real-time. The relative timing between action potentials of individual neurons as well as the temporal pattern among multiple neurons, or neuronal assemblies, is considered an important factor governing memory and learning in the brain. Artificially changing timings between arbitrary sets of spiking neurons with our system could provide a "knob" to tune information processing in the network. Close https://www.frontiersin.org/articles/10.3389/fncir.2012.00121/full doi:10.3389/fncir.2012.00121 Close
2010
	Livi, Paolo; Heer, Flavio; Frey, Urs; Bakkum, Douglas J; Hierlemann, Andreas Compact voltage and current stimulation buffer for high-density microelectrode arrays Journal Article IEEE Transactions on Biomedical Circuits and Systems, 4 (6), pp. 372-378, 2010, ISSN: 19324545. Abstract \| Links \| BibTeX \| Tags: ETH-CMOS-MEA, MEA Technology, Stimulation @article{Livi2010, title = {Compact voltage and current stimulation buffer for high-density microelectrode arrays}, author = {Paolo Livi and Flavio Heer and Urs Frey and Douglas J Bakkum and Andreas Hierlemann}, url = {http://ieeexplore.ieee.org/document/5617318/}, doi = {10.1109/TBCAS.2010.2080676}, issn = {19324545}, year = {2010}, date = {2010-11-01}, journal = {IEEE Transactions on Biomedical Circuits and Systems}, volume = {4}, number = {6}, pages = {372-378}, abstract = {We report on a compact (0.02 mm2 ) buffer for both voltage and current stimulation of electrogenic cells on a complementary metal-oxide semiconductor microelectrode array. In voltage mode, the circuit is a high-current class-AB voltage follower, based on a local common-mode feedback (LCMFB) amplifier. In current mode, the circuit is a current conveyor of type II, using the same LCMFB amplifier with cascode stages to increase the gain. The circuit shows good linearity in the 0.5-3.5 V input range and has extensively been used for stimulation of neuronal cultures.}, keywords = {ETH-CMOS-MEA, MEA Technology, Stimulation}, pubstate = {published}, tppubtype = {article} } Close We report on a compact (0.02 mm2 ) buffer for both voltage and current stimulation of electrogenic cells on a complementary metal-oxide semiconductor microelectrode array. In voltage mode, the circuit is a high-current class-AB voltage follower, based on a local common-mode feedback (LCMFB) amplifier. In current mode, the circuit is a current conveyor of type II, using the same LCMFB amplifier with cascode stages to increase the gain. The circuit shows good linearity in the 0.5-3.5 V input range and has extensively been used for stimulation of neuronal cultures. Close http://ieeexplore.ieee.org/document/5617318/ doi:10.1109/TBCAS.2010.2080676 Close
2006
	Heer, Flavio; Hafizovic, Sadik; Franks, Wendy; Blau, Axel; Ziegler, Christiane; Hierlemann, Andreas CMOS microelectrode array for bidirectional interaction with neuronal networks Journal Article IEEE Journal of Solid-State Circuits, 41 (7), pp. 1620-1629, 2006, ISSN: 0018-9200. Abstract \| Links \| BibTeX \| Tags: 2D Neuronal Culture, ETH-CMOS-MEA, MEA Technology, Neuronal Networks, Stimulation @article{Heer2006, title = {CMOS microelectrode array for bidirectional interaction with neuronal networks}, author = {Flavio Heer and Sadik Hafizovic and Wendy Franks and Axel Blau and Christiane Ziegler and Andreas Hierlemann}, url = {http://ieeexplore.ieee.org/document/1644873/}, doi = {10.1109/JSSC.2006.873677}, issn = {0018-9200}, year = {2006}, date = {2006-07-07}, journal = {IEEE Journal of Solid-State Circuits}, volume = {41}, number = {7}, pages = {1620-1629}, abstract = {A CMOS metal-electrode-based micro system for bidirectional communication (stimulation and recording) with neuronal cells in vitro is presented. The chip overcomes the interconnect challenge that limits today's bidirectional microelectrode arrays. The microsystem has been fabricated in an industrial CMOS technology with several post-CMOS processing steps to realize 128 biocompatible electrodes and to ensure chip stability in physiological saline. The system comprises all necessary control circuitry and on-chip A/D and D/A conversion. A modular design has been implemented, where individual stimulation- and signal-conditioning circuitry units are associated with each electrode. Stimulation signals with a resolution of 8 bits can be sent to any subset of electrodes at a rate of 60 kHz, while all electrodes of the chip are continuously sampled at a rate of 20 kHz. The circuitry at each electrode can be individually reset to its operating point in order to suppress artifacts evoked by the stimulation pulses. Biological measurements from cultured neuronal networks originating from dissociated cortical tissue of fertilized chicken eggs with amplitudes of up to 500 muVpp are presented.}, keywords = {2D Neuronal Culture, ETH-CMOS-MEA, MEA Technology, Neuronal Networks, Stimulation}, pubstate = {published}, tppubtype = {article} } Close A CMOS metal-electrode-based micro system for bidirectional communication (stimulation and recording) with neuronal cells in vitro is presented. The chip overcomes the interconnect challenge that limits today's bidirectional microelectrode arrays. The microsystem has been fabricated in an industrial CMOS technology with several post-CMOS processing steps to realize 128 biocompatible electrodes and to ensure chip stability in physiological saline. The system comprises all necessary control circuitry and on-chip A/D and D/A conversion. A modular design has been implemented, where individual stimulation- and signal-conditioning circuitry units are associated with each electrode. Stimulation signals with a resolution of 8 bits can be sent to any subset of electrodes at a rate of 60 kHz, while all electrodes of the chip are continuously sampled at a rate of 20 kHz. The circuitry at each electrode can be individually reset to its operating point in order to suppress artifacts evoked by the stimulation pulses. Biological measurements from cultured neuronal networks originating from dissociated cortical tissue of fertilized chicken eggs with amplitudes of up to 500 muVpp are presented. Close http://ieeexplore.ieee.org/document/1644873/ doi:10.1109/JSSC.2006.873677 Close

Selected Publications

High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels

Presenting measurements of neuronal preparations with a novel CMOS-based microelectrode array at high-spatiotemporal-resolution on subcellular, cellular, and network level.

J. Müller, M. Ballini, P. Livi, Y. Chen, M. Radivojevic, A. Shadmani, V. Viswam, I. L. Jones, M. Fiscella, R. Diggelmann, A. Stettler, U. Frey, D. J. Bakkum, and A. Hierlemann, “High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels,” Lab Chip, vol. 15, no. 13, pp. 2767–2780, May 2015.

Revealing Neuronal Function through Microelectrode Array Recordings

Reviewing the current understanding of microelectrode signals and the techniques for analyzing them, with focus on the ongoing advancements in microelectrode technology (in vivo and in vitro) and recent advanced microelectrode array measurement methods that facilitate the understanding of single neurons and network function.

M. E. J. Obien, K. Deligkaris, T. Bullmann, D. J. Bakkum, and U. Frey, “Revealing Neuronal Function through Microelectrode Array Recordings,” Front. Neurosci., 8:423, Jan 2015.

A 1024-Channel CMOS Microelectrode Array With 26,400 Electrodes for Recording and Stimulation of Electrogenic Cells In Vitro

A high-resolution CMOS-based microelectrode array featuring 1,024 low-noise readout channels, 26,400 electrodes at a density of 3,265 electrodes per mm², including on-chip 10bit ADCs and consuming only 75 mW.

M. Ballini, J. Muller, P. Livi, Y. Chen, U. Frey, A. Stettler, A. Shadmani, V. Viswam, I. L. Jones, D. Jackel, M. Radivojevic, M. K. Lewandowska, W. Gong, M. Fiscella, D. J. Bakkum, F. Heer, and A. Hierlemann, “A 1024-Channel CMOS Microelectrode Array With 26,400 Electrodes for Recording and Stimulation of Electrogenic Cells In Vitro,” IEEE Journal of Solid-State Circuits, vol. 49, no. 11, pp. 2705-2719, 2014.

Tracking axonal action potential propagation on a high-density microelectrode array across hundreds of sites

Demonstrating a method to electrically visualize action potential propagation on axons and revealing
large variations in velocity.

D. J. Bakkum, U. Frey, M. Radivojevic, T. L. Russell, J. Muller, M. Fiscella, H. Takahashi, and A. Hierlemann, “Tracking axonal action potential propagation on a high-density microelectrode array across hundreds of sites,” Nature Communications, 4:2181, Jul 2013.

Microelectronic System for High-Resolution Mapping of Extracellular Electric Fields Applied to Brain Slices

Recording and modeling extracellular action potentials of Purkinje cells at subcellular resolution.

U. Frey, U. Egert, F. Heer, S. Hafizovic, and A. Hierlemann, “Microelectronic System for High-Resolution Mapping of Extracellular Electric Fields Applied to Brain Slices,” Biosensors and Bioelectronics, vol. 24, no. 7, pp. 2191-2198, 2009.

Modulation of Cardiomyocyte Electrical Properties Using Regulated Bone Morphogenetic Protein-2 Expression

Controlling BMP-2 expression to modulate the electrophysiological properties of cardiomyocytes using an HD-MEA for detailed monitoring.

C. D. Sanchez-Bustamante, U. Frey, J. M. Kelm, A. Hierlemann, and M. Fussenegger,
“Modulation of Cardiomyocyte Electrical Properties Using Regulated Bone Morphogenetic Protein-2 Expression,” Tissue Engineering Part A, vol. 14, no. 12, pp. 1969-1988, 2008.

2023
	Cai, Hongwei; Ao, Zheng; Tian, Chunhui; Wu, Zhuhao; Liu, Hongcheng; Tchieu, Jason; Gu, Mingxia; Mackie, Ken; Guo, Feng Brain organoid reservoir computing for artificial intelligence Journal Article Nature Electronics, 2023. Abstract \| Links \| BibTeX \| Tags: 3D Culture, HD-MEA, Machine Learning, MaxOne, MEA Technology, Organoids, Stimulation @article{Cai2023b, title = {Brain organoid reservoir computing for artificial intelligence}, author = {Hongwei Cai and Zheng Ao and Chunhui Tian and Zhuhao Wu and Hongcheng Liu and Jason Tchieu and Mingxia Gu and Ken Mackie and Feng Guo }, url = {https://www.nature.com/articles/s41928-023-01069-w}, doi = {10.1038/s41928-023-01069-w}, year = {2023}, date = {2023-12-11}, journal = {Nature Electronics}, abstract = {Brain-inspired computing hardware aims to emulate the structure and working principles of the brain and could be used to address current limitations in artificial intelligence technologies. However, brain-inspired silicon chips are still limited in their ability to fully mimic brain function as most examples are built on digital electronic principles. Here we report an artificial intelligence hardware approach that uses adaptive reservoir computation of biological neural networks in a brain organoid. In this approach—which is termed Brainoware—computation is performed by sending and receiving information from the brain organoid using a high-density multielectrode array. By applying spatiotemporal electrical stimulation, nonlinear dynamics and fading memory properties are achieved, as well as unsupervised learning from training data by reshaping the organoid functional connectivity. We illustrate the practical potential of this technique by using it for speech recognition and nonlinear equation prediction in a reservoir computing framework.}, keywords = {3D Culture, HD-MEA, Machine Learning, MaxOne, MEA Technology, Organoids, Stimulation}, pubstate = {published}, tppubtype = {article} } Close Brain-inspired computing hardware aims to emulate the structure and working principles of the brain and could be used to address current limitations in artificial intelligence technologies. However, brain-inspired silicon chips are still limited in their ability to fully mimic brain function as most examples are built on digital electronic principles. Here we report an artificial intelligence hardware approach that uses adaptive reservoir computation of biological neural networks in a brain organoid. In this approach—which is termed Brainoware—computation is performed by sending and receiving information from the brain organoid using a high-density multielectrode array. By applying spatiotemporal electrical stimulation, nonlinear dynamics and fading memory properties are achieved, as well as unsupervised learning from training data by reshaping the organoid functional connectivity. We illustrate the practical potential of this technique by using it for speech recognition and nonlinear equation prediction in a reservoir computing framework. Close https://www.nature.com/articles/s41928-023-01069-w doi:10.1038/s41928-023-01069-w Close
	Elliott, Matthew A T; Schweiger, Hunter E; Robbins, Ash; Vera-Choqqueccota, Samira; Ehrlich, Drew; Hernandez, Sebastian; Voitiuk, Kateryna; Geng, Jinghui; Sevetson, Jess L; Core, Cordero; Rosen, Yohei M; Teodorescu, Mircea; Wagner, Nico O; Haussler, David; Mostajo-Radji, Mohammed A Internet-Connected Cortical Organoids for Project-Based Stem Cell and Neuroscience Education Journal Article eNeuro, 2023. Abstract \| Links \| BibTeX \| Tags: 3D Culture, Activity Scan Assay, HD-MEA, IPSC, MaxOne, MEA Technology, Organoids, Spike Sorting, Stimulation @article{Elliott2023, title = {Internet-Connected Cortical Organoids for Project-Based Stem Cell and Neuroscience Education}, author = {Matthew A. T. Elliott and Hunter E. Schweiger and Ash Robbins and Samira Vera-Choqqueccota and Drew Ehrlich and Sebastian Hernandez and Kateryna Voitiuk and Jinghui Geng and Jess L. Sevetson and Cordero Core and Yohei M. Rosen and Mircea Teodorescu and Nico O. Wagner and David Haussler and Mohammed A. Mostajo-Radji}, url = {https://www.eneuro.org/lookup/doi/10.1523/ENEURO.0308-23.2023}, doi = {10.1523/ENEURO.0308-23.2023}, year = {2023}, date = {2023-11-28}, journal = {eNeuro}, abstract = {The introduction of Internet-connected technologies to the classroom has the potential to revolutionize STEM education by allowing students to perform experiments in complex models that are unattainable in traditional teaching laboratories. By connecting laboratory equipment to the cloud, we introduce students to experimentation in pluripotent stem cell (PSC)-derived cortical organoids in two different settings: using microscopy to monitor organoid growth in an introductory tissue culture course and using high-density (HD) multielectrode arrays (MEAs) to perform neuronal stimulation and recording in an advanced neuroscience mathematics course. We demonstrate that this approach develops interest in stem cell and neuroscience in the students of both courses. All together, we propose cloud technologies as an effective and scalable approach for complex project-based university training.}, keywords = {3D Culture, Activity Scan Assay, HD-MEA, IPSC, MaxOne, MEA Technology, Organoids, Spike Sorting, Stimulation}, pubstate = {published}, tppubtype = {article} } Close The introduction of Internet-connected technologies to the classroom has the potential to revolutionize STEM education by allowing students to perform experiments in complex models that are unattainable in traditional teaching laboratories. By connecting laboratory equipment to the cloud, we introduce students to experimentation in pluripotent stem cell (PSC)-derived cortical organoids in two different settings: using microscopy to monitor organoid growth in an introductory tissue culture course and using high-density (HD) multielectrode arrays (MEAs) to perform neuronal stimulation and recording in an advanced neuroscience mathematics course. We demonstrate that this approach develops interest in stem cell and neuroscience in the students of both courses. All together, we propose cloud technologies as an effective and scalable approach for complex project-based university training. Close https://www.eneuro.org/lookup/doi/10.1523/ENEURO.0308-23.2023 doi:10.1523/ENEURO.0308-23.2023 Close
	Kobayashi, Toki; Shimba, Kenta; Kotani, Kiyoshi; Jimbo, Yasuhiko Measuring and Inducing the Plasticity of Single-Neuron Scale at Multiple Points Conference ISSS Xplore, 2023. Abstract \| Links \| BibTeX \| Tags: HD-MEA, MaxOne, Primary Neuronal Cell Culture, Stimulation @conference{Kobayashi2023, title = {Measuring and Inducing the Plasticity of Single-Neuron Scale at Multiple Points}, author = {Toki Kobayashi and Kenta Shimba and Kiyoshi Kotani and Yasuhiko Jimbo}, url = {https://ieeexplore.ieee.org/abstract/document/10322076}, doi = {10.1109/BMEiCON60347.2023.10322076}, year = {2023}, date = {2023-11-22}, journal = {IEEE Xplore}, publisher = {ISSS Xplore}, abstract = {Information processing in the brain is supported by the plasticity of the connections of neurons in biological neuronal networks. There is a gap between our understanding on plasticity of individual connections and that of the neuronal networks. Here, we aimed to induce and measure plasticity at the connections of individual neurons and at the network level. We achieved inducing plasticity with a combination of single cell stimulation by optogenetics and high-resolution extracellular potentials recording by high-density microelectrode array. Spike timing plasticity in a single neuron was successfully measured. Although we also tried to induce synaptic potentiation by tetanus stimulation, no significant change was observed. In the future, we will investigate how the plasticity of individual connections changes dynamics of entire network.}, keywords = {HD-MEA, MaxOne, Primary Neuronal Cell Culture, Stimulation}, pubstate = {published}, tppubtype = {conference} } Close Information processing in the brain is supported by the plasticity of the connections of neurons in biological neuronal networks. There is a gap between our understanding on plasticity of individual connections and that of the neuronal networks. Here, we aimed to induce and measure plasticity at the connections of individual neurons and at the network level. We achieved inducing plasticity with a combination of single cell stimulation by optogenetics and high-resolution extracellular potentials recording by high-density microelectrode array. Spike timing plasticity in a single neuron was successfully measured. Although we also tried to induce synaptic potentiation by tetanus stimulation, no significant change was observed. In the future, we will investigate how the plasticity of individual connections changes dynamics of entire network. Close https://ieeexplore.ieee.org/abstract/document/10322076 doi:10.1109/BMEiCON60347.2023.10322076 Close
	Duru, Jens; Rüfenacht, Arielle; Löhle, Josephine; Pozzi, Marcello; Forró, Csaba; Ledermann, Linus; Bernardi, Aeneas; Matter, Michael; Renia, André; Simona, Benjamin; Tringides, Christina M; Bernhard, Stéphane; Ihle, Stephan J; Hengsteler, Julian; Maurer, Benedikt; Zhanga, Xinyu; Nakatsuka, Nako Driving electrochemical reactions at the microscale using CMOS microelectrode arrays Journal Article Lab on a Chip, 2023, ISSN: 1473-0189. Abstract \| Links \| BibTeX \| Tags: HD-MEA, MaxOne, Primary Neuronal Cell Culture, Stimulation @article{Duru2023c, title = {Driving electrochemical reactions at the microscale using CMOS microelectrode arrays}, author = {Jens Duru and Arielle Rüfenacht and Josephine Löhle and Marcello Pozzi and Csaba Forró and Linus Ledermann and Aeneas Bernardi and Michael Matter and André Renia and Benjamin Simona and Christina M. Tringides and Stéphane Bernhard and Stephan J. Ihle and Julian Hengsteler and Benedikt Maurer and Xinyu Zhanga and Nako Nakatsuka}, url = {https://pubs.rsc.org/en/content/articlelanding/2023/lc/d3lc00630a}, doi = {10.1039/D3LC00630A}, issn = {1473-0189}, year = {2023}, date = {2023-10-30}, journal = {Lab on a Chip}, abstract = {Precise control of pH values at electrode interfaces enables the systematic investigation of pH-dependent processes by electrochemical means. In this work, we employed high-density complementary metal-oxide-semiconductor (CMOS) microelectrode arrays (MEAs) as miniaturized systems to induce and confine electrochemical reactions in areas corresponding to the pitch of single electrodes (17.5 μm). First, we present a strategy for generating localized pH patterns on the surface of the CMOS MEA with unprecedented spatial resolution. Leveraging the versatile routing capabilities of the switch matrix beneath the CMOS MEA, we created arbitrary combinations of anodic and cathodic electrodes and hence pH patterns. Moreover, we utilized the system to produce polymeric surface patterns by additive and subtractive methods. For additive patterning, we controlled the in situ formation of polydopamine at the microelectrode surface through oxidation of free dopamine above a threshold pH > 8.5. For subtractive patterning, we removed cell-adhesive poly-L-lysine from the electrode surface and backfilled the voids with antifouling polymers. Such polymers were chosen to provide a proof-of-concept application of controlling neuronal growth via electrochemically-induced patterns on the CMOS MEA surface. Importantly, our platform is compatible with commercially available high-density MEAs and requires no custom equipment, rendering the findings generalizable and accessible.}, keywords = {HD-MEA, MaxOne, Primary Neuronal Cell Culture, Stimulation}, pubstate = {published}, tppubtype = {article} } Close Precise control of pH values at electrode interfaces enables the systematic investigation of pH-dependent processes by electrochemical means. In this work, we employed high-density complementary metal-oxide-semiconductor (CMOS) microelectrode arrays (MEAs) as miniaturized systems to induce and confine electrochemical reactions in areas corresponding to the pitch of single electrodes (17.5 μm). First, we present a strategy for generating localized pH patterns on the surface of the CMOS MEA with unprecedented spatial resolution. Leveraging the versatile routing capabilities of the switch matrix beneath the CMOS MEA, we created arbitrary combinations of anodic and cathodic electrodes and hence pH patterns. Moreover, we utilized the system to produce polymeric surface patterns by additive and subtractive methods. For additive patterning, we controlled the in situ formation of polydopamine at the microelectrode surface through oxidation of free dopamine above a threshold pH > 8.5. For subtractive patterning, we removed cell-adhesive poly-L-lysine from the electrode surface and backfilled the voids with antifouling polymers. Such polymers were chosen to provide a proof-of-concept application of controlling neuronal growth via electrochemically-induced patterns on the CMOS MEA surface. Importantly, our platform is compatible with commercially available high-density MEAs and requires no custom equipment, rendering the findings generalizable and accessible. Close https://pubs.rsc.org/en/content/articlelanding/2023/lc/d3lc00630a doi:10.1039/D3LC00630A Close
	Duru, Jens; Maurer, Benedikt; Doran, Ciara Giles; Jelitto, Robert; Küchler, Joël; Ihle, Stephan J; Ruff, Tobias; John, Robert; Genocchi, Barbara; Vörös, János Investigation of the input-output relationship of engineered neural networks using high-density microelectrode arrays Journal Article Biosensors and Bioelectronics, 2023. Abstract \| Links \| BibTeX \| Tags: 2D Neuronal Culture, HD-MEA, MaxOne, Stimulation @article{Duru2023b, title = {Investigation of the input-output relationship of engineered neural networks using high-density microelectrode arrays}, author = {Jens Duru and Benedikt Maurer and Ciara Giles Doran and Robert Jelitto and Joël Küchler and Stephan J. Ihle and Tobias Ruff and Robert John and Barbara Genocchi and János Vörös }, url = {https://www.sciencedirect.com/science/article/pii/S095656632300533X?via%3Dihub}, doi = {https://doi.org/10.1016/j.bios.2023.115591}, year = {2023}, date = {2023-08-18}, journal = {Biosensors and Bioelectronics}, abstract = {Bottom-up neuroscience utilizes small, engineered biological neural networks to study neuronal activity in systems of reduced complexity. We present a platform that establishes up to six independent networks formed by primary rat neurons on planar complementary metal–oxide–semiconductor (CMOS) microelectrode arrays (MEAs). We introduce an approach that allows repetitive stimulation and recording of network activity at any of the over 700 electrodes underlying a network. We demonstrate that the continuous application of a repetitive super-threshold stimulus yields a reproducible network answer within a 15 ms post-stimulus window. This response can be tracked with high spatiotemporal resolution across the whole extent of the network. Moreover, we show that the location of the stimulation plays a significant role in the networks' early response to the stimulus. By applying a stimulation pattern to all network-underlying electrodes in sequence, the sensitivity of the whole network to the stimulus can be visualized. We demonstrate that microchannels reduce the voltage stimulation threshold and induce the strongest network response. By varying the stimulation amplitude and frequency we reveal discrete network transition points. Finally, we introduce vector fields to follow stimulation-induced spike propagation pathways within the network. Overall we show that our defined neural networks on CMOS MEAs enable us to elicit highly reproducible activity patterns that can be precisely modulated by stimulation amplitude, stimulation frequency and the site of stimulation.}, keywords = {2D Neuronal Culture, HD-MEA, MaxOne, Stimulation}, pubstate = {published}, tppubtype = {article} } Close Bottom-up neuroscience utilizes small, engineered biological neural networks to study neuronal activity in systems of reduced complexity. We present a platform that establishes up to six independent networks formed by primary rat neurons on planar complementary metal–oxide–semiconductor (CMOS) microelectrode arrays (MEAs). We introduce an approach that allows repetitive stimulation and recording of network activity at any of the over 700 electrodes underlying a network. We demonstrate that the continuous application of a repetitive super-threshold stimulus yields a reproducible network answer within a 15 ms post-stimulus window. This response can be tracked with high spatiotemporal resolution across the whole extent of the network. Moreover, we show that the location of the stimulation plays a significant role in the networks' early response to the stimulus. By applying a stimulation pattern to all network-underlying electrodes in sequence, the sensitivity of the whole network to the stimulus can be visualized. We demonstrate that microchannels reduce the voltage stimulation threshold and induce the strongest network response. By varying the stimulation amplitude and frequency we reveal discrete network transition points. Finally, we introduce vector fields to follow stimulation-induced spike propagation pathways within the network. Overall we show that our defined neural networks on CMOS MEAs enable us to elicit highly reproducible activity patterns that can be precisely modulated by stimulation amplitude, stimulation frequency and the site of stimulation. Close https://www.sciencedirect.com/science/article/pii/S095656632300533X?via%3Dihub doi:https://doi.org/10.1016/j.bios.2023.115591 Close
	Duru, Jens; Maurer, Benedikt; Doran, Ciara Giles; Jelitto, Robert; Küchler, Joël; Ihle, Stephan J; Ruff, Tobias; John, Robert; Genocchi, Barbara; Vörös, János Investigation of the input-output relationship of engineered neural networks using high-density microelectrode arrays Journal Article SSRN, 2023. Abstract \| Links \| BibTeX \| Tags: HD-MEA, MaxOne, MEA Technology, Primary Neuronal Cell Culture, Spike Sorting, Stimulation @article{Duru2023, title = {Investigation of the input-output relationship of engineered neural networks using high-density microelectrode arrays}, author = {Jens Duru and Benedikt Maurer and Ciara Giles Doran and Robert Jelitto and Joël Küchler and Stephan J. Ihle and Tobias Ruff and Robert John and Barbara Genocchi and János Vörös}, url = {https://www.ssrn.com/abstract=4427959}, doi = {DOI: 10.2139/ssrn.4427959}, year = {2023}, date = {2023-04-24}, journal = {SSRN}, abstract = {Bottom-up neuroscience utilizes small, engineered biological neural networks to study neuronal activity in systems of reduced complexity. We present a platform that establishes up to six independent networks formed by primary rat neurons on planar complementary metal–oxide–semiconductor (CMOS) microelectrode arrays (MEAs). We introduce an approach that allows repetitive stimulation and recording of network activity at any of the over 700 electrodes underlying a network. We demonstrate that the continuous application of a repetitive super-threshold stimulus yields a reproducible network answer within a 15 ms post-stimulus window. This response can be tracked with high spatiotemporal resolution across the whole extent of the network. Moreover, we show that the location of the stimulation plays a significant role in the networks’ early response to the stimulus. By applying a stimulation pattern to all network-underlying electrodes in sequence, the sensitivity of the whole network to the stimulus can be visualized. We demonstrate that microchannels reduce the voltage stimulation threshold and induce the strongest network response. By varying the stimulation amplitude and frequency we reveal discrete network transition points. Finally, we introduce vector fields to follow stimulation-induced spike propagation pathways within the network. Overall we show that our defined neural networks on CMOS MEAs enable us to elicit highly reproducible activity patterns that can be precisely modulated by stimulation amplitude, stimulation frequency and the site of stimulation.}, keywords = {HD-MEA, MaxOne, MEA Technology, Primary Neuronal Cell Culture, Spike Sorting, Stimulation}, pubstate = {published}, tppubtype = {article} } Close Bottom-up neuroscience utilizes small, engineered biological neural networks to study neuronal activity in systems of reduced complexity. We present a platform that establishes up to six independent networks formed by primary rat neurons on planar complementary metal–oxide–semiconductor (CMOS) microelectrode arrays (MEAs). We introduce an approach that allows repetitive stimulation and recording of network activity at any of the over 700 electrodes underlying a network. We demonstrate that the continuous application of a repetitive super-threshold stimulus yields a reproducible network answer within a 15 ms post-stimulus window. This response can be tracked with high spatiotemporal resolution across the whole extent of the network. Moreover, we show that the location of the stimulation plays a significant role in the networks’ early response to the stimulus. By applying a stimulation pattern to all network-underlying electrodes in sequence, the sensitivity of the whole network to the stimulus can be visualized. We demonstrate that microchannels reduce the voltage stimulation threshold and induce the strongest network response. By varying the stimulation amplitude and frequency we reveal discrete network transition points. Finally, we introduce vector fields to follow stimulation-induced spike propagation pathways within the network. Overall we show that our defined neural networks on CMOS MEAs enable us to elicit highly reproducible activity patterns that can be precisely modulated by stimulation amplitude, stimulation frequency and the site of stimulation. Close https://www.ssrn.com/abstract=4427959 doi:DOI: 10.2139/ssrn.4427959 Close
	Cai, Hongwei; Ao, Zheng; Tian, Chunhui; Wu, Zhuhao; Liu, Hongcheng; Tchieu, Jason; Gu, Mingxia; Mackie, Ken; and Guo, Feng Brain Organoid Computing for Artificial Intelligence Journal Article bioRxiv, 2023. Abstract \| Links \| BibTeX \| Tags: HD-MEA, Machine Learning, MaxOne, MEA Technology, Modeling, Organoids, Stimulation @article{Cai2023, title = {Brain Organoid Computing for Artificial Intelligence}, author = {Hongwei Cai and Zheng Ao and Chunhui Tian and Zhuhao Wu and Hongcheng Liu and Jason Tchieu and Mingxia Gu and Ken Mackie and and Feng Guo}, url = {https://www.biorxiv.org/content/10.1101/2023.02.28.530502v1}, doi = {10.1101/2023.02.28.530502}, year = {2023}, date = {2023-03-01}, journal = {bioRxiv}, abstract = {Brain-inspired hardware emulates the structure and working principles of a biological brain and may address the hardware bottleneck for fast-growing artificial intelligence (AI). Current brain-inspired silicon chips are promising but still limit their power to fully mimic brain function for AI computing. Here, we develop Brainoware, living AI hardware that harnesses the computation power of 3D biological neural networks in a brain organoid. Brain-like 3D in vitro cultures compute by receiving and sending information via a multielectrode array. Applying spatiotemporal electrical stimulation, this approach not only exhibits nonlinear dynamics and fading memory properties but also learns from training data. Further experiments demonstrate real-world applications in solving non-linear equations. This approach may provide new insights into AI hardware. }, keywords = {HD-MEA, Machine Learning, MaxOne, MEA Technology, Modeling, Organoids, Stimulation}, pubstate = {published}, tppubtype = {article} } Close Brain-inspired hardware emulates the structure and working principles of a biological brain and may address the hardware bottleneck for fast-growing artificial intelligence (AI). Current brain-inspired silicon chips are promising but still limit their power to fully mimic brain function for AI computing. Here, we develop Brainoware, living AI hardware that harnesses the computation power of 3D biological neural networks in a brain organoid. Brain-like 3D in vitro cultures compute by receiving and sending information via a multielectrode array. Applying spatiotemporal electrical stimulation, this approach not only exhibits nonlinear dynamics and fading memory properties but also learns from training data. Further experiments demonstrate real-world applications in solving non-linear equations. This approach may provide new insights into AI hardware. Close https://www.biorxiv.org/content/10.1101/2023.02.28.530502v1 doi:10.1101/2023.02.28.530502 Close
2022
	Sawada, Haruto; Wake, Naoki; Sasabuchi, Kazuhiro; Takamatsu, Jun; Takahashi, Hirokazu; Ikeuchi, Katsushi Design strategies for controlling neuron-connected robots using reinforcement learning Journal Article arXiv, 2022. Abstract \| Links \| BibTeX \| Tags: Machine Learning, MaxOne, Stimulation @article{Sawada2022, title = {Design strategies for controlling neuron-connected robots using reinforcement learning}, author = {Haruto Sawada and Naoki Wake and Kazuhiro Sasabuchi and Jun Takamatsu and Hirokazu Takahashi and Katsushi Ikeuchi}, url = {https://arxiv.org/abs/2203.15290}, doi = {https://doi.org/10.48550/arXiv.2203.15290}, year = {2022}, date = {2022-03-29}, journal = {arXiv}, abstract = {Despite the growing interest in robot control utilizing the computation of biological neurons, context-dependent behavior by neuron-connected robots remains a challenge. Context-dependent behavior here is defined as behavior that is not the result of a simple sensory-motor coupling, but rather based on an understanding of the task goal. This paper proposes design principles for training neuron-connected robots based on task goals to achieve context-dependent behavior. First, we employ deep reinforcement learning (RL) to enable training that accounts for goal achievements. Second, we propose a neuron simulator as a probability distribution based on recorded neural data, aiming to represent physiologically valid neural dynamics while avoiding complex modeling with high computational costs. Furthermore, we propose to update the simulators during the training to bridge the gap between the simulation and the real settings. The experiments showed that the robot gradually learned context-dependent behaviors in pole balancing and robot navigation tasks. Moreover, the learned policies were valid for neural simulators based on novel neural data, and the task performance increased by updating the simulators during training. These results suggest the effectiveness of the proposed design principle for the context-dependent behavior of neuron-connected robots.}, keywords = {Machine Learning, MaxOne, Stimulation}, pubstate = {published}, tppubtype = {article} } Close Despite the growing interest in robot control utilizing the computation of biological neurons, context-dependent behavior by neuron-connected robots remains a challenge. Context-dependent behavior here is defined as behavior that is not the result of a simple sensory-motor coupling, but rather based on an understanding of the task goal. This paper proposes design principles for training neuron-connected robots based on task goals to achieve context-dependent behavior. First, we employ deep reinforcement learning (RL) to enable training that accounts for goal achievements. Second, we propose a neuron simulator as a probability distribution based on recorded neural data, aiming to represent physiologically valid neural dynamics while avoiding complex modeling with high computational costs. Furthermore, we propose to update the simulators during the training to bridge the gap between the simulation and the real settings. The experiments showed that the robot gradually learned context-dependent behaviors in pole balancing and robot navigation tasks. Moreover, the learned policies were valid for neural simulators based on novel neural data, and the task performance increased by updating the simulators during training. These results suggest the effectiveness of the proposed design principle for the context-dependent behavior of neuron-connected robots. Close https://arxiv.org/abs/2203.15290 doi:https://doi.org/10.48550/arXiv.2203.15290 Close
2021
	Kubota, Tomoyuki; Takahashi, Hirokazu; and Nakajima, Kohei Unifying framework for information processing in stochastically driven dynamical systems Journal Article Physical Review Research, 2021. Abstract \| Links \| BibTeX \| Tags: 2D Neuronal Culture, HD-MEA, MaxOne, Primary Neuronal Cell Culture, Stimulation @article{Kubota2023, title = {Unifying framework for information processing in stochastically driven dynamical systems}, author = {Tomoyuki Kubota and Hirokazu Takahashi and and Kohei Nakajima}, url = {https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.3.043135}, doi = {https://doi.org/10.1103/PhysRevResearch.3.043135}, year = {2021}, date = {2021-11-23}, journal = {Physical Review Research}, abstract = {A dynamical system is an information processing apparatus that encodes input streams from the external environment to its state and processes them through state transitions. The information processing capacity (IPC) is an excellent tool that comprehensively evaluates these processed inputs, providing details of unknown information processing in black box systems; however, this measure can be applied only to time-invariant systems. This paper extends the applicable range to time-variant systems and further reveals that the IPC is equivalent to coefficients of polynomial chaos (PC) expansion in more general dynamical systems. To achieve this objective, we tackle three issues. First, we establish a connection between the IPC for time-invariant systems and PC expansion, which is a type of polynomial expansion using orthogonal functions of input history as bases. We prove that the IPC corresponds to the squared norm of the coefficient vector of the basis in the PC expansion. Second, we show that an input following an arbitrary distribution can be used for the IPC, removing previous restrictions to specific input distributions. Third, we extend the conventional orthogonal bases to functions of both time and input history and propose the IPC for time-variant systems. To show the significance of our approach, we demonstrate that our measure can reveal information representations in not only machine learning networks but also a real, cultured neural network. Our generalized measure paves the way for unveiling the information processing capabilities of a wide variety of physical dynamics which have been left behind in nature.}, keywords = {2D Neuronal Culture, HD-MEA, MaxOne, Primary Neuronal Cell Culture, Stimulation}, pubstate = {published}, tppubtype = {article} } Close A dynamical system is an information processing apparatus that encodes input streams from the external environment to its state and processes them through state transitions. The information processing capacity (IPC) is an excellent tool that comprehensively evaluates these processed inputs, providing details of unknown information processing in black box systems; however, this measure can be applied only to time-invariant systems. This paper extends the applicable range to time-variant systems and further reveals that the IPC is equivalent to coefficients of polynomial chaos (PC) expansion in more general dynamical systems. To achieve this objective, we tackle three issues. First, we establish a connection between the IPC for time-invariant systems and PC expansion, which is a type of polynomial expansion using orthogonal functions of input history as bases. We prove that the IPC corresponds to the squared norm of the coefficient vector of the basis in the PC expansion. Second, we show that an input following an arbitrary distribution can be used for the IPC, removing previous restrictions to specific input distributions. Third, we extend the conventional orthogonal bases to functions of both time and input history and propose the IPC for time-variant systems. To show the significance of our approach, we demonstrate that our measure can reveal information representations in not only machine learning networks but also a real, cultured neural network. Our generalized measure paves the way for unveiling the information processing capabilities of a wide variety of physical dynamics which have been left behind in nature. Close https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.3.043135 doi:https://doi.org/10.1103/PhysRevResearch.3.043135 Close
2019
	Ronchi, Silvia; Fiscella, Michele; Marchetti, Camilla; Viswam, Vijay; Muller, Jan; Frey, Urs; Hierlemann, Andreas Single-Cell Electrical Stimulation Using CMOS-Based High-Density Microelectrode Arrays Journal Article Frontiers in Neuroscience, 13 , 2019, ISSN: 1662-453X . Abstract \| Links \| BibTeX \| Tags: ETH-CMOS-MEA, Stimulation @article{Ronchi2019, title = {Single-Cell Electrical Stimulation Using CMOS-Based High-Density Microelectrode Arrays}, author = {Silvia Ronchi and Michele Fiscella and Camilla Marchetti and Vijay Viswam and Jan Muller and Urs Frey and Andreas Hierlemann}, url = {https://www.frontiersin.org/article/10.3389/fnins.2019.00208 }, doi = {10.3389/fnins.2019.00208 }, issn = {1662-453X }, year = {2019}, date = {2019-03-13}, journal = {Frontiers in Neuroscience}, volume = {13}, abstract = {Non-invasive electrical stimulation can be used to study and control neural activity in the brain or to alleviate somatosensory dysfunctions. One intriguing prospect is to precisely stimulate individual targeted neurons. Here, we investigated single-neuron current and voltage stimulation in vitro using high-density microelectrode arrays featuring 26’400 bidirectional electrodes at a pitch of 17.5 µm and an electrode area of 5 × 9 µm². We determined optimal waveforms, amplitudes and durations for both stimulation modes. Owing to the high spatial resolution of our arrays and the close proximity of the electrodes to the respective neurons, we were able to stimulate the axon initial segments (AIS) with charges of less than 2 picoCoulombs. This resulted in minimal artifact production and reliable readout of stimulation efficiency directly at the soma of the stimulated cell. Stimulation signals as low as 70 mV or 100 nA,with pulse durations as short as 18 µs, yielded measurable action potential initiation and propagation. We found that the required stimulation signal amplitudes decreased with cell growth and development and that stimulation efficiency did not improve at higher electric fields generated by simultaneous multi-electrode stimulation.}, keywords = {ETH-CMOS-MEA, Stimulation}, pubstate = {published}, tppubtype = {article} } Close Non-invasive electrical stimulation can be used to study and control neural activity in the brain or to alleviate somatosensory dysfunctions. One intriguing prospect is to precisely stimulate individual targeted neurons. Here, we investigated single-neuron current and voltage stimulation in vitro using high-density microelectrode arrays featuring 26’400 bidirectional electrodes at a pitch of 17.5 µm and an electrode area of 5 × 9 µm². We determined optimal waveforms, amplitudes and durations for both stimulation modes. Owing to the high spatial resolution of our arrays and the close proximity of the electrodes to the respective neurons, we were able to stimulate the axon initial segments (AIS) with charges of less than 2 picoCoulombs. This resulted in minimal artifact production and reliable readout of stimulation efficiency directly at the soma of the stimulated cell. Stimulation signals as low as 70 mV or 100 nA,with pulse durations as short as 18 µs, yielded measurable action potential initiation and propagation. We found that the required stimulation signal amplitudes decreased with cell growth and development and that stimulation efficiency did not improve at higher electric fields generated by simultaneous multi-electrode stimulation. Close https://www.frontiersin.org/article/10.3389/fnins.2019.00208 doi:10.3389/fnins.2019.00208 Close
2018
	Ronchi, Silvia; Fiscella, Michele; Marchetti, Camilla; Viswam, Vijay; Müller, Jan; Frey, Urs; Hierlemann, Andreas Single-neuron sub-cellular-resolution electrical stimulation with high-density microelectrode arrays Conference Contribution 174.05 , Society for Neuroscience (SfN) Meeting San Diego, CA, USA, 2018. Abstract \| Links \| BibTeX \| Tags: ETH-CMOS-MEA, Stimulation @conference{Ronchi2018, title = {Single-neuron sub-cellular-resolution electrical stimulation with high-density microelectrode arrays}, author = {Silvia Ronchi and Michele Fiscella and Camilla Marchetti and Vijay Viswam and Jan Müller and Urs Frey and Andreas Hierlemann}, url = {https://abstractsonline.com/pp8/#!/4649/presentation/24382}, year = {2018}, date = {2018-11-04}, volume = {Contribution 174.05}, address = {San Diego, CA, USA}, organization = {Society for Neuroscience (SfN) Meeting}, abstract = {Non-invasive electrical stimulation is a consolidated technique to study and control neural activity in the brain and peripheral nervous system. It is used, e.g., for controlling Parkinson's disease or to induce sensation in paralyzed patients (Armenta Salas et al., 2018), as well as for attempts to restore vision (Fan et al., 2018; Grosberg et al., 2017) and hearing (Wilson & Dorman, 2008). A common requirement in electrical stimulation is the precise and controlled stimulation of individual targeted neurons. For achieving this purpose, it is necessary that electrodes can stimulate and record extracellular signals at sub-cellular resolution. Furthermore, it is important to design efficient stimulation pulses to be delivered to the neurons. In the present work we used an CMOS-based high-density microelectrode array (HD-MEA) (Ballini et al., 2014), featuring 26’400 bidirectional electrodes with a pitch of 17.5 µm, which was designed for in-vitro applications. This high-resolution was used to test electrical stimulation parameters in vitro, which then could potentially be adapted to elicit single-cell action potentials in vivo. In this work we used different stimulation parameters, such as waveforms, amplitudes and durations (Grosberg et al., 2017; Wagenaar, Pine, & Potter, 2004), using 5x9 µm² electrodes to target sub-cellular structures in single neurons. E-18 Wistar rat cortical neurons were stimulated at days-in-vitro 10, 15, 20 and 25 using randomized voltage and current stimulation modalities. Axon initial segments of individual neurons (Radivojevic et al., 2016) were targeted for stimulation, enabled by the HD-MEA device. We found that voltage biphasic anodic-cathodic waveforms were less efficient than biphasic cathodic-anodic waveforms in eliciting action potentials in a single neuron. Moreover, it was possible to detect action potentials directly at the cell soma, which ensured a reliable confirmation of successful neuron stimulation. Finally, HD-MEA technology enabled to elicit action potentials in single-neurons embedded in high-density cell cultures. The obtained results can be used to optimize in vivo single-cell targeting for stimulation and read-out.}, keywords = {ETH-CMOS-MEA, Stimulation}, pubstate = {published}, tppubtype = {conference} } Close Non-invasive electrical stimulation is a consolidated technique to study and control neural activity in the brain and peripheral nervous system. It is used, e.g., for controlling Parkinson's disease or to induce sensation in paralyzed patients (Armenta Salas et al., 2018), as well as for attempts to restore vision (Fan et al., 2018; Grosberg et al., 2017) and hearing (Wilson & Dorman, 2008). A common requirement in electrical stimulation is the precise and controlled stimulation of individual targeted neurons. For achieving this purpose, it is necessary that electrodes can stimulate and record extracellular signals at sub-cellular resolution. Furthermore, it is important to design efficient stimulation pulses to be delivered to the neurons. In the present work we used an CMOS-based high-density microelectrode array (HD-MEA) (Ballini et al., 2014), featuring 26’400 bidirectional electrodes with a pitch of 17.5 µm, which was designed for in-vitro applications. This high-resolution was used to test electrical stimulation parameters in vitro, which then could potentially be adapted to elicit single-cell action potentials in vivo. In this work we used different stimulation parameters, such as waveforms, amplitudes and durations (Grosberg et al., 2017; Wagenaar, Pine, & Potter, 2004), using 5x9 µm² electrodes to target sub-cellular structures in single neurons. E-18 Wistar rat cortical neurons were stimulated at days-in-vitro 10, 15, 20 and 25 using randomized voltage and current stimulation modalities. Axon initial segments of individual neurons (Radivojevic et al., 2016) were targeted for stimulation, enabled by the HD-MEA device. We found that voltage biphasic anodic-cathodic waveforms were less efficient than biphasic cathodic-anodic waveforms in eliciting action potentials in a single neuron. Moreover, it was possible to detect action potentials directly at the cell soma, which ensured a reliable confirmation of successful neuron stimulation. Finally, HD-MEA technology enabled to elicit action potentials in single-neurons embedded in high-density cell cultures. The obtained results can be used to optimize in vivo single-cell targeting for stimulation and read-out. Close https://abstractsonline.com/pp8/#!/4649/presentation/24382 Close
	Ronchi, Silvia; Fiscella, Michele; Muller, Jan; Viswam, Vijay; Frey, Urs; Hierlemann, Andreas Single-cell electrical stimulation with CMOS-based high-density microelectrode arrays Conference 11th International Meeting on Substrate Integrated Microelectrode Arrays (MEA Meeting) Reutlingen, Germany, 2018. Abstract \| Links \| BibTeX \| Tags: ETH-CMOS-MEA, Stimulation @conference{Ronchi2018b, title = {Single-cell electrical stimulation with CMOS-based high-density microelectrode arrays}, author = {Silvia Ronchi and Michele Fiscella and Jan Muller and Vijay Viswam and Urs Frey and Andreas Hierlemann}, url = {https://www.frontiersin.org/10.3389/conf.fncel.2018.38.00086/event_abstract}, doi = {10.3389/conf.fncel.2018.38.00086}, year = {2018}, date = {2018-07-04}, address = {Reutlingen, Germany}, organization = {11th International Meeting on Substrate Integrated Microelectrode Arrays (MEA Meeting)}, abstract = {The main goal of this work was to explore electrical stimulation parameters that reproducibly and precisely elicit action potentials in single neurons (Wagenaar et al. 2004). We compared voltage and current modalities’ and their efficacy in activating single neurons; we also studied the related stimulation artifacts. For our studies, we used a CMOS-based MEA featuring 26400 electrodes at 17.5 µm pitch (Ballini et al. 2014). }, keywords = {ETH-CMOS-MEA, Stimulation}, pubstate = {published}, tppubtype = {conference} } Close The main goal of this work was to explore electrical stimulation parameters that reproducibly and precisely elicit action potentials in single neurons (Wagenaar et al. 2004). We compared voltage and current modalities’ and their efficacy in activating single neurons; we also studied the related stimulation artifacts. For our studies, we used a CMOS-based MEA featuring 26400 electrodes at 17.5 µm pitch (Ballini et al. 2014). Close https://www.frontiersin.org/10.3389/conf.fncel.2018.38.00086/event_abstract doi:10.3389/conf.fncel.2018.38.00086 Close
	Yuan, Xinyue; Hierlemann, Andreas; Frey, Urs Dual-mode Microelectrode Array with 20k-electrodes and High SNR for High-Throughput Extracellular Recording and Stimulation Conference 11th International Meeting on Substrate Integrated Microelectrode Arrays (MEA Meeting) Reutlingen, Germany, 2018. Abstract \| Links \| BibTeX \| Tags: ETH-CMOS-MEA, Stimulation @conference{Yuan2018, title = {Dual-mode Microelectrode Array with 20k-electrodes and High SNR for High-Throughput Extracellular Recording and Stimulation}, author = {Xinyue Yuan and Andreas Hierlemann and Urs Frey}, url = {https://https://www.frontiersin.org/Community/AbstractDetails.aspx?ABS_DOI=10.3389/conf.fncel.2018.38.00088&eid=5473&sname=MEA_Meeting_2018_%7C_11th_International_Meeting_on_Substrate_Integrated_Microelectrode_Arrays}, doi = {10.3389/conf.fncel.2018.38.00088}, year = {2018}, date = {2018-07-04}, address = {Reutlingen, Germany}, organization = {11th International Meeting on Substrate Integrated Microelectrode Arrays (MEA Meeting)}, abstract = {Recording and analysis of neuronal signals can provide much insight into how neurons process information and communicate with each other. Recent advancements of microelectrode-array (MEA) technology provide unprecedented means to study neuronal signals and network behavior in in vitro and in vivo applications [1], [2]. The trade-off between noise performance, power consumption and electrode density, however, remains a major challenge in MEA design. To balance this tradeoff, we designed a Dual-mode (DM) MEA that combines two major types of readout schemes, i.e., the active-pixel-sensor (APS) and switch-matrix (SM) schemes, in order to achieve high electrode density and high signal-to-noise ratio (SNR) at the same time. Based on a previous prototype [3], the new DM-MEA has shown to be a useful tool for in-vitro neuroscience studies, especially for network studies}, keywords = {ETH-CMOS-MEA, Stimulation}, pubstate = {published}, tppubtype = {conference} } Close Recording and analysis of neuronal signals can provide much insight into how neurons process information and communicate with each other. Recent advancements of microelectrode-array (MEA) technology provide unprecedented means to study neuronal signals and network behavior in in vitro and in vivo applications [1], [2]. The trade-off between noise performance, power consumption and electrode density, however, remains a major challenge in MEA design. To balance this tradeoff, we designed a Dual-mode (DM) MEA that combines two major types of readout schemes, i.e., the active-pixel-sensor (APS) and switch-matrix (SM) schemes, in order to achieve high electrode density and high signal-to-noise ratio (SNR) at the same time. Based on a previous prototype [3], the new DM-MEA has shown to be a useful tool for in-vitro neuroscience studies, especially for network studies Close https://https://www.frontiersin.org/Community/AbstractDetails.aspx?ABS_DOI=10.33[...] doi:10.3389/conf.fncel.2018.38.00088 Close
2017
	Radivojevic, Milos; Franke, Felix; Altermatt, Michael; Müller, Jan; Hierlemann, Andreas; Bakkum, Douglas J Tracking individual action potentials throughout mammalian axonal arbors Journal Article eLife, 6 , pp. 1-23, 2017, ISSN: 2050-084X. Abstract \| Links \| BibTeX \| Tags: Data Analysis, ETH-CMOS-MEA, Neuronal Networks, Stimulation @article{Radivojevic2017, title = {Tracking individual action potentials throughout mammalian axonal arbors}, author = {Milos Radivojevic and Felix Franke and Michael Altermatt and Jan Müller and Andreas Hierlemann and Douglas J Bakkum}, url = {https://elifesciences.org/articles/30198}, doi = {10.7554/eLife.30198}, issn = {2050-084X}, year = {2017}, date = {2017-10-09}, journal = {eLife}, volume = {6}, pages = {1-23}, abstract = {Axons are neuronal processes specialized for conduction of action potentials (APs). The timing and temporal precision of APs when they reach each of the synapses are fundamentally important for information processing in the brain. Due to small diameters of axons, direct recording of single AP transmission is challenging. Consequently, most knowledge about axonal conductance derives from modeling studies or indirect measurements. We demonstrate a method to noninvasively and directly record individual APs propagating along millimeter-length axonal arbors in cortical cultures with hundreds of microelectrodes at microsecond temporal resolution. We find that cortical axons conduct single APs with high temporal precision (~100 µs arrival time jitter per mm length) and reliability: in more than 8,000,000 recorded APs, we did not observe any conduction or branch-point failures. Upon high-frequency stimulation at 100 Hz, successive became slower, and their arrival time precision decreased by 20% and 12% for the 100th AP, respectively.}, keywords = {Data Analysis, ETH-CMOS-MEA, Neuronal Networks, Stimulation}, pubstate = {published}, tppubtype = {article} } Close Axons are neuronal processes specialized for conduction of action potentials (APs). The timing and temporal precision of APs when they reach each of the synapses are fundamentally important for information processing in the brain. Due to small diameters of axons, direct recording of single AP transmission is challenging. Consequently, most knowledge about axonal conductance derives from modeling studies or indirect measurements. We demonstrate a method to noninvasively and directly record individual APs propagating along millimeter-length axonal arbors in cortical cultures with hundreds of microelectrodes at microsecond temporal resolution. We find that cortical axons conduct single APs with high temporal precision (~100 µs arrival time jitter per mm length) and reliability: in more than 8,000,000 recorded APs, we did not observe any conduction or branch-point failures. Upon high-frequency stimulation at 100 Hz, successive became slower, and their arrival time precision decreased by 20% and 12% for the 100th AP, respectively. Close https://elifesciences.org/articles/30198 doi:10.7554/eLife.30198 Close
2016
	Radivojevic, Milos; Jäckel, David; Altermatt, Michael; Müller, Jan; Viswam, Vijay; Hierlemann, Andreas; Bakkum, Douglas J Electrical Identification and Selective Microstimulation of Neuronal Compartments Based on Features of Extracellular Action Potentials Journal Article Scientific Reports, 6 (1), pp. 1-20, 2016, ISSN: 2045-2322. Abstract \| Links \| BibTeX \| Tags: ETH-CMOS-MEA, Neuronal Networks, Stimulation @article{Radivojevic2016, title = {Electrical Identification and Selective Microstimulation of Neuronal Compartments Based on Features of Extracellular Action Potentials}, author = {Milos Radivojevic and David Jäckel and Michael Altermatt and Jan Müller and Vijay Viswam and Andreas Hierlemann and Douglas J Bakkum}, url = {http://www.nature.com/articles/srep31332}, doi = {10.1038/srep31332}, issn = {2045-2322}, year = {2016}, date = {2016-08-11}, journal = {Scientific Reports}, volume = {6}, number = {1}, pages = {1-20}, abstract = {A detailed, high-spatiotemporal-resolution characterization of neuronal responses to local electrical fields and the capability of precise extracellular microstimulation of selected neurons are pivotal for studying and manipulating neuronal activity and circuits in networks and for developing neural prosthetics. Here, we studied cultured neocortical neurons by using high-density microelectrode arrays and optical imaging, complemented by the patch-clamp technique, and with the aim to correlate morphological and electrical features of neuronal compartments with their responsiveness to extracellular stimulation. We developed strategies to electrically identify any neuron in the network, while subcellular spatial resolution recording of extracellular action potential (AP) traces enabled their assignment to the axon initial segment (AIS), axonal arbor and proximal somatodendritic compartments. Stimulation at the AIS required low voltages and provided immediate, selective and reliable neuronal activation, whereas stimulation at the soma required high voltages and produced delayed and unreliable responses. Subthreshold stimulation at the soma depolarized the somatic membrane potential without eliciting APs.}, keywords = {ETH-CMOS-MEA, Neuronal Networks, Stimulation}, pubstate = {published}, tppubtype = {article} } Close A detailed, high-spatiotemporal-resolution characterization of neuronal responses to local electrical fields and the capability of precise extracellular microstimulation of selected neurons are pivotal for studying and manipulating neuronal activity and circuits in networks and for developing neural prosthetics. Here, we studied cultured neocortical neurons by using high-density microelectrode arrays and optical imaging, complemented by the patch-clamp technique, and with the aim to correlate morphological and electrical features of neuronal compartments with their responsiveness to extracellular stimulation. We developed strategies to electrically identify any neuron in the network, while subcellular spatial resolution recording of extracellular action potential (AP) traces enabled their assignment to the axon initial segment (AIS), axonal arbor and proximal somatodendritic compartments. Stimulation at the AIS required low voltages and provided immediate, selective and reliable neuronal activation, whereas stimulation at the soma required high voltages and produced delayed and unreliable responses. Subthreshold stimulation at the soma depolarized the somatic membrane potential without eliciting APs. Close http://www.nature.com/articles/srep31332 doi:10.1038/srep31332 Close
2013
	Müller, Jan; Bakkum, Douglas J; Hierlemann, Andreas Sub-millisecond closed-loop feedback stimulation between arbitrary sets of individual neurons. Journal Article Frontiers in Neural Circuits, 6 , pp. 121, 2013, ISSN: 1662-5110. Abstract \| Links \| BibTeX \| Tags: ETH-CMOS-MEA, MEA Technology, Neuronal Networks, Stimulation @article{Muller2012, title = {Sub-millisecond closed-loop feedback stimulation between arbitrary sets of individual neurons.}, author = {Jan Müller and Douglas J Bakkum and Andreas Hierlemann}, url = {https://www.frontiersin.org/articles/10.3389/fncir.2012.00121/full}, doi = {10.3389/fncir.2012.00121}, issn = {1662-5110}, year = {2013}, date = {2013-01-10}, journal = {Frontiers in Neural Circuits}, volume = {6}, pages = {121}, abstract = {We present a system to artificially correlate the spike timing between sets of arbitrary neurons that were interfaced to a complementary metal-oxide-semiconductor (CMOS) high-density microelectrode array (MEA). The system features a novel reprogrammable and flexible event engine unit to detect arbitrary spatio-temporal patterns of recorded action potentials and is capable of delivering sub-millisecond closed-loop feedback of electrical stimulation upon trigger events in real-time. The relative timing between action potentials of individual neurons as well as the temporal pattern among multiple neurons, or neuronal assemblies, is considered an important factor governing memory and learning in the brain. Artificially changing timings between arbitrary sets of spiking neurons with our system could provide a "knob" to tune information processing in the network.}, keywords = {ETH-CMOS-MEA, MEA Technology, Neuronal Networks, Stimulation}, pubstate = {published}, tppubtype = {article} } Close We present a system to artificially correlate the spike timing between sets of arbitrary neurons that were interfaced to a complementary metal-oxide-semiconductor (CMOS) high-density microelectrode array (MEA). The system features a novel reprogrammable and flexible event engine unit to detect arbitrary spatio-temporal patterns of recorded action potentials and is capable of delivering sub-millisecond closed-loop feedback of electrical stimulation upon trigger events in real-time. The relative timing between action potentials of individual neurons as well as the temporal pattern among multiple neurons, or neuronal assemblies, is considered an important factor governing memory and learning in the brain. Artificially changing timings between arbitrary sets of spiking neurons with our system could provide a "knob" to tune information processing in the network. Close https://www.frontiersin.org/articles/10.3389/fncir.2012.00121/full doi:10.3389/fncir.2012.00121 Close
2010
	Livi, Paolo; Heer, Flavio; Frey, Urs; Bakkum, Douglas J; Hierlemann, Andreas Compact voltage and current stimulation buffer for high-density microelectrode arrays Journal Article IEEE Transactions on Biomedical Circuits and Systems, 4 (6), pp. 372-378, 2010, ISSN: 19324545. Abstract \| Links \| BibTeX \| Tags: ETH-CMOS-MEA, MEA Technology, Stimulation @article{Livi2010, title = {Compact voltage and current stimulation buffer for high-density microelectrode arrays}, author = {Paolo Livi and Flavio Heer and Urs Frey and Douglas J Bakkum and Andreas Hierlemann}, url = {http://ieeexplore.ieee.org/document/5617318/}, doi = {10.1109/TBCAS.2010.2080676}, issn = {19324545}, year = {2010}, date = {2010-11-01}, journal = {IEEE Transactions on Biomedical Circuits and Systems}, volume = {4}, number = {6}, pages = {372-378}, abstract = {We report on a compact (0.02 mm2 ) buffer for both voltage and current stimulation of electrogenic cells on a complementary metal-oxide semiconductor microelectrode array. In voltage mode, the circuit is a high-current class-AB voltage follower, based on a local common-mode feedback (LCMFB) amplifier. In current mode, the circuit is a current conveyor of type II, using the same LCMFB amplifier with cascode stages to increase the gain. The circuit shows good linearity in the 0.5-3.5 V input range and has extensively been used for stimulation of neuronal cultures.}, keywords = {ETH-CMOS-MEA, MEA Technology, Stimulation}, pubstate = {published}, tppubtype = {article} } Close We report on a compact (0.02 mm2 ) buffer for both voltage and current stimulation of electrogenic cells on a complementary metal-oxide semiconductor microelectrode array. In voltage mode, the circuit is a high-current class-AB voltage follower, based on a local common-mode feedback (LCMFB) amplifier. In current mode, the circuit is a current conveyor of type II, using the same LCMFB amplifier with cascode stages to increase the gain. The circuit shows good linearity in the 0.5-3.5 V input range and has extensively been used for stimulation of neuronal cultures. Close http://ieeexplore.ieee.org/document/5617318/ doi:10.1109/TBCAS.2010.2080676 Close
2006
	Heer, Flavio; Hafizovic, Sadik; Franks, Wendy; Blau, Axel; Ziegler, Christiane; Hierlemann, Andreas CMOS microelectrode array for bidirectional interaction with neuronal networks Journal Article IEEE Journal of Solid-State Circuits, 41 (7), pp. 1620-1629, 2006, ISSN: 0018-9200. Abstract \| Links \| BibTeX \| Tags: 2D Neuronal Culture, ETH-CMOS-MEA, MEA Technology, Neuronal Networks, Stimulation @article{Heer2006, title = {CMOS microelectrode array for bidirectional interaction with neuronal networks}, author = {Flavio Heer and Sadik Hafizovic and Wendy Franks and Axel Blau and Christiane Ziegler and Andreas Hierlemann}, url = {http://ieeexplore.ieee.org/document/1644873/}, doi = {10.1109/JSSC.2006.873677}, issn = {0018-9200}, year = {2006}, date = {2006-07-07}, journal = {IEEE Journal of Solid-State Circuits}, volume = {41}, number = {7}, pages = {1620-1629}, abstract = {A CMOS metal-electrode-based micro system for bidirectional communication (stimulation and recording) with neuronal cells in vitro is presented. The chip overcomes the interconnect challenge that limits today's bidirectional microelectrode arrays. The microsystem has been fabricated in an industrial CMOS technology with several post-CMOS processing steps to realize 128 biocompatible electrodes and to ensure chip stability in physiological saline. The system comprises all necessary control circuitry and on-chip A/D and D/A conversion. A modular design has been implemented, where individual stimulation- and signal-conditioning circuitry units are associated with each electrode. Stimulation signals with a resolution of 8 bits can be sent to any subset of electrodes at a rate of 60 kHz, while all electrodes of the chip are continuously sampled at a rate of 20 kHz. The circuitry at each electrode can be individually reset to its operating point in order to suppress artifacts evoked by the stimulation pulses. Biological measurements from cultured neuronal networks originating from dissociated cortical tissue of fertilized chicken eggs with amplitudes of up to 500 muVpp are presented.}, keywords = {2D Neuronal Culture, ETH-CMOS-MEA, MEA Technology, Neuronal Networks, Stimulation}, pubstate = {published}, tppubtype = {article} } Close A CMOS metal-electrode-based micro system for bidirectional communication (stimulation and recording) with neuronal cells in vitro is presented. The chip overcomes the interconnect challenge that limits today's bidirectional microelectrode arrays. The microsystem has been fabricated in an industrial CMOS technology with several post-CMOS processing steps to realize 128 biocompatible electrodes and to ensure chip stability in physiological saline. The system comprises all necessary control circuitry and on-chip A/D and D/A conversion. A modular design has been implemented, where individual stimulation- and signal-conditioning circuitry units are associated with each electrode. Stimulation signals with a resolution of 8 bits can be sent to any subset of electrodes at a rate of 60 kHz, while all electrodes of the chip are continuously sampled at a rate of 20 kHz. The circuitry at each electrode can be individually reset to its operating point in order to suppress artifacts evoked by the stimulation pulses. Biological measurements from cultured neuronal networks originating from dissociated cortical tissue of fertilized chicken eggs with amplitudes of up to 500 muVpp are presented. Close http://ieeexplore.ieee.org/document/1644873/ doi:10.1109/JSSC.2006.873677 Close

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