@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}
}

@article{Franke2016,
title = {Structures of Neural Correlation and How They Favor Coding},
author = {Felix Franke and Michele Fiscella and Maksim Sevelev and Botond Roska and Andreas Hierlemann and Rava {Azeredo da Silveira}},
url = {http://www.sciencedirect.com/science/article/pii/S0896627315011393?via%3Dihub},
doi = {10.1016/j.neuron.2015.12.037},
issn = {10974199},
year = {2016},
date = {2016-01-20},
journal = {Neuron},
volume = {89},
number = {2},
pages = {409-422},
publisher = {Elsevier Inc.},
abstract = {The neural representation of information suffers from "noise"-the trial-to-trial variability in the response of neurons. The impact of correlated noise upon population coding has been debated, but a direct connection between theory and experiment remains tenuous. Here, we substantiate this connection and propose a refined theoretical picture. Using simultaneous recordings from a population of direction-selective retinal ganglion cells, we demonstrate that coding benefits from noise correlations. The effect is appreciable already in small populations, yet it is a collective phenomenon. Furthermore, the stimulus-dependent structure of correlation is key. We develop simple functional models that capture the stimulus-dependent statistics. We then use them to quantify the performance of population coding, which depends upon interplays of feature sensitivities and noise correlations in the population. Because favorable structures of correlation emerge robustly in circuits with noisy, nonlinear elements, they will arise and benefit coding beyond the confines of retina. Coding in the brain suffers from the variability of neural responses. Using experiment and theory, Franke et al. show that this "noise" comes with a particular structure, which emerges from circuit properties and which counteracts the harmful effect of variability.},
keywords = {Data Analysis, ETH-CMOS-MEA, Neuronal Networks, Retina},
pubstate = {published},
tppubtype = {article}
}

@article{Fiscella2015,
title = {Visual coding with a population of direction-selective neurons},
author = {Michele Fiscella and Felix Franke and Karl Farrow and Jan Müller and Botond Roska and Rava {Azeredo da Silveira} and Andreas Hierlemann},
url = {http://jn.physiology.org/lookup/doi/10.1152/jn.00919.2014},
doi = {10.1152/jn.00919.2014},
issn = {0022-3077},
year = {2015},
date = {2015-08-19},
journal = {Journal of Neurophysiology},
volume = {114},
number = {4},
pages = {2485-2499},
abstract = {The brain decodes the visual scene from the action potentials of ∼20 retinal ganglion cell types. Among the retinal ganglion cells, direction-selective ganglion cells (DSGCs) encode motion direction. Several studies have focused on the encoding or decoding of motion direction by recording multiunit activity, mainly in the visual cortex. In this study, we simultaneously recorded from all four types of ON-OFF DSGCs of the rabbit retina using a microelectronics-based high-density microelectrode array (HDMEA) and decoded their concerted activity using probabilistic and linear decoders. Furthermore, we investigated how the modification of stimulus parameters (velocity, size, angle of moving object) and the use of different tuning curve fits influenced decoding precision. Finally, we simulated ON-OFF DSGC activity, based on real data, in order to understand how tuning curve widths and the angular distribution of the cells' preferred directions influence decoding performance. We found that probabilistic decoding strategies outperformed, on average, linear methods and that decoding precision was robust to changes in stimulus parameters such as velocity. The removal of noise correlations among cells, by random shuffling trials, caused a drop in decoding precision. Moreover, we found that tuning curves are broad in order to minimize large errors at the expense of a higher average error, and that the retinal direction-selective system would not substantially benefit, on average, from having more than four types of ON-OFF DSGCs or from a perfect alignment of the cells' preferred directions.},
keywords = {Data Analysis, ETH-CMOS-MEA, Retina},
pubstate = {published},
tppubtype = {article}
}

@article{Bakkum2014,
title = {Parameters for burst detection},
author = {Douglas J Bakkum and Milos Radivojevic and Urs Frey and Felix Franke and Andreas Hierlemann and Hirokazu Takahashi},
url = {http://journal.frontiersin.org/article/10.3389/fncom.2013.00193/abstract},
doi = {10.3389/fncom.2013.00193},
issn = {1662-5188},
year = {2014},
date = {2014-01-13},
journal = {Frontiers in Computational Neuroscience},
volume = {7},
pages = {1-12},
abstract = {Bursts of action potentials within neurons and throughout networks are believed to serve roles in how neurons handle and store information, both in vivo and in vitro. Accurate detection of burst occurrences and durations are therefore crucial for many studies. A number of algorithms have been proposed to do so, but a standard method has not been adopted. This is due, in part, to many algorithms requiring the adjustment of multiple ad-hoc parameters and further post-hoc criteria in order to produce satisfactory results. Here, we broadly catalog existing approaches and present a new approach requiring the selection of only a single parameter: the number of spikes N comprising the smallest burst to consider. A burst was identified if N spikes occurred in less than T ms, where the threshold T was automatically determined from observing a probability distribution of inter-spike-intervals. Performance was compared vs. different classes of detectors on data gathered from in vitro neuronal networks grown over microelectrode arrays. Our approach offered a number of useful features including: a simple implementation, no need for ad-hoc or post-hoc criteria, and precise assignment of burst boundary time points. Unlike existing approaches, detection was not biased toward larger bursts, allowing identification and analysis of a greater range of neuronal and network dynamics.},
keywords = {Data Analysis, ETH-CMOS-MEA},
pubstate = {published},
tppubtype = {article}
}

@article{Bakkum2013,
title = {Tracking axonal action potential propagation on a high-density microelectrode array across hundreds of sites},
author = {Douglas J Bakkum and Urs Frey and Milos Radivojevic and Thomas L Russell and Jan Müller and Michele Fiscella and Hirokazu Takahashi and Andreas Hierlemann},
url = {http://www.nature.com/doifinder/10.1038/ncomms3181},
doi = {10.1038/ncomms3181},
issn = {2041-1723},
year = {2013},
date = {2013-07-19},
journal = {Nature Communications},
volume = {4},
pages = {1-12},
abstract = {Axons are traditionally considered stable transmission cables, but evidence of the regulation of action potential propagation demonstrates that axons may have more important roles. However, their small diameters render intracellular recordings challenging, and low-magnitude extracellular signals are difficult to detect and assign. Better experimental access to axonal function would help to advance this field. Here we report methods to electrically visualize action potential propagation and network topology in cortical neurons grown over custom arrays, which contain 11,011 microelectrodes and are fabricated using complementary metal oxide semiconductor technology. Any neuron lying on the array can be recorded at high spatio-temporal resolution, and simultaneously precisely stimulated with little artifact. We find substantial velocity differences occurring locally within single axons, suggesting that the temporal control of a neuron's output may contribute to neuronal information processing.},
keywords = {Data Analysis, ETH-CMOS-MEA, Neuronal Networks},
pubstate = {published},
tppubtype = {article}
}