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Public Library of Science, PLoS Computational Biology, 6(11), p. e1004275, 2015

DOI: 10.1371/journal.pcbi.1004275

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Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models

Journal article published in 2015 by Skander Mensi, Christian Antonio Pozzorini, Olivier Hagens, Richard Naud, Christof Koch ORCID, Wulfram Gerstner
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Single-neuron models are useful not only for studying the emergent properties of neural circuits in large-scale simulations, but also for extracting and summarizing in a principled way the information contained in electrophysiological recordings. Here we demonstrate that, using a convex optimization procedure we previously introduced, a Generalized Integrate-and-Fire model can be accurately fitted with a limited amount of data. The model is capable of predicting both the spiking activity and the subthreshold dynamics of different cell types, and can be used for online characterization of neuronal properties. A protocol is proposed that, combined with emergent technologies for automatic patch-clamp recordings, permits automated, in vitro high-throughput characterization of single neurons.