@article{Zhao2023,
title = {A CMOS-based highly scalable flexible neural electrode interface},
author = {Eric T. Zhao and Jacob M. Hull and Nofar Mintz Hemed and Hasan Ulusan and Julian Bartram and Anqi Zhang and Pingyu Wang and Albert Pham and Silvia Ronchi, John R. Huguenard and Andreas Hierlemann and Nicholas A. Melosh},
url = {https://www.science.org/doi/10.1126/sciadv.adf9524},
doi = {DOI: 10.1126/sciadv.adf9524},
year = {2023},
date = {2023-06-07},
journal = {Science Advances},
abstract = {Perception, thoughts, and actions are encoded by the coordinated activity of large neuronal populations spread over large areas. However, existing electrophysiological devices are limited by their scalability in capturing this cortex-wide activity. Here, we developed an electrode connector based on an ultra-conformable thin-film electrode array that self-assembles onto silicon microelectrode arrays enabling multithousand channel counts at a millimeter scale. The interconnects are formed using microfabricated electrode pads suspended by thin support arms, termed Flex2Chip. Capillary-assisted assembly drives the pads to deform toward the chip surface, and van der Waals forces maintain this deformation, establishing Ohmic contact. Flex2Chip arrays successfully measured extracellular action potentials ex vivo and resolved micrometer scale seizure propagation trajectories in epileptic mice. We find that seizure dynamics in absence epilepsy in the Scn8a+/− model do not have constant propagation trajectories.},
keywords = {3D Culture, HD-MEA, MaxOne, Other Tissues, Slices},
pubstate = {published},
tppubtype = {article}
}

@article{Buccino2022,
title = {A multi-modal fitting approach to construct single-neuron models with patch clamp and high-density microelectrode arrays},
author = {Buccino, Alessio Paolo; Damart, Tanguy; Bartram, Julian; Mandge, Darshan; Xue, Xiaohan; Zbili, Mickael; Gänswein, Tobias; Jaquier, Aurélien; Emmenegger, Vishalini; Markram, Henry; Hierlemann, Andreas; Van Geit, Werner.},
doi = {https://doi.org/10.1101/2022.08.03.502468},
year = {2022},
date = {2022-08-11},
journal = {bioRxiv},
abstract = {In computational neuroscience, multicompartment models are among the most biophysically realistic representations of single neurons. Constructing such models usually involves the use of the patch-clamp technique to record somatic voltage signals under different experimental conditions. The experimental data are then used to fit the many parameters of the model. While patching of the soma is currently the gold-standard approach to build multicompartment models, several studies have also evidenced a richness of dynamics in dendritic and axonal sections. Recording from the soma alone makes it hard to observe and correctly parameterize the activity of non-somatic compartments.

In order to provide a richer set of data as input to multicompartment models, we here investigate the combination of somatic patch-clamp recordings with recordings of high-density micro-electrode arrays (HD-MEAs). HD-MEAs enable the observation of extracellular potentials and neural activity of neuronal compartments at sub-cellular resolution.

In this work, we introduce a novel framework to combine patch-clamp and HD-MEA data to construct multicompartment models. We first validate our method on a ground-truth model with known parameters and show that the use of features extracted from extracellular signals, in addition to intracellular ones, yields models enabling better fits than using intracellular features alone. We also demonstrate our procedure using experimental data by constructing cell models from in vitro cell cultures.

The proposed multi-modal fitting procedure has the potential to augment the modeling efforts of the computational neuroscience community and to provide the field with neuronal models that are more realistic and can be better validated.

Author Summary Multicompartment models are one of the most biophysically detailed representations of single neurons. The vast majority of these models are built using experimental data from somatic recordings. However, neurons are much more than just their soma and one needs recordings from distal neurites to build an accurate model. In this article, we combine the patch-clamp technique with extracellular high-density microelectrode arrays (HD-MEAs) to compensate this shortcoming. In fact, HD-MEAs readouts allow one to record the neuronal signal in the entire axonal arbor. We show that the proposed multi-modal strategy is superior to the use of patch clamp alone using an existing model as ground-truth. Finally, we show an application of this strategy on experimental data from cultured neurons.},
keywords = {2D Neuronal Culture, Activity Scan Assay, Axon Tracking Assay, HD-MEA, MaxOne, Other Tissues, Publication, Stimulation Assay},
pubstate = {published},
tppubtype = {article}
}