Automated in vivo patch-clamp evaluation of extracellular multielectrode array spike recording capability

Author:

Allen Brian D.1,Moore-Kochlacs Caroline12,Bernstein Jacob G.1,Kinney Justin P.13,Scholvin Jorg1,Seoane Luís F.4,Chronopoulos Chris3,Lamantia Charlie3,Kodandaramaiah Suhasa B.15,Tegmark Max4,Boyden Edward S.1

Affiliation:

1. Media Lab and McGovern Institute for Brain Research, Departments of Biological Engineering and Brain and Cognitive Sciences, and Koch Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts

2. Department of Neuroscience, Boston University, Boston, Massachusetts

3. Leaflabs, LLC, Cambridge, Massachusetts

4. Department of Physics and Kavli Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts

5. Department of Mechanical Engineering, University of Minnesota, Minneapolis, Minnesota

Abstract

Much innovation is currently aimed at improving the number, density, and geometry of electrodes on extracellular multielectrode arrays for in vivo recording of neural activity in the mammalian brain. To choose a multielectrode array configuration for a given neuroscience purpose, or to reveal design principles of future multielectrode arrays, it would be useful to have a systematic way of evaluating the spike recording capability of such arrays. We describe an automated system that performs robotic patch-clamp recording of a neuron being simultaneously recorded via an extracellular multielectrode array. By recording a patch-clamp data set from a neuron while acquiring extracellular recordings from the same neuron, we can evaluate how well the extracellular multielectrode array captures the spiking information from that neuron. To demonstrate the utility of our system, we show that it can provide data from the mammalian cortex to evaluate how the spike sorting performance of a close-packed extracellular multielectrode array is affected by bursting, which alters the shape and amplitude of spikes in a train. We also introduce an algorithmic framework to help evaluate how the number of electrodes in a multielectrode array affects spike sorting, examining how adding more electrodes yields data that can be spike sorted more easily. Our automated methodology may thus help with the evaluation of new electrode designs and configurations, providing empirical guidance on the kinds of electrodes that will be optimal for different brain regions, cell types, and species, for improving the accuracy of spike sorting. NEW & NOTEWORTHY We present an automated strategy for evaluating the spike recording performance of an extracellular multielectrode array, by enabling simultaneous recording of a neuron with both such an array and with patch clamp. We use our robot and accompanying algorithms to evaluate the performance of multielectrode arrays on supporting spike sorting.

Funder

HHS | National Institutes of Health (NIH)

Howard Hughes Medical Institute (HHMI)

Publisher

American Physiological Society

Subject

Physiology,General Neuroscience

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