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

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.

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Plos Computational Biology, 11, 4, e1004275
San Francisco, Public Library of Science
Funded by the Swiss National Science Foundation (SNSF) and the European Community under HBP.
This work was supported by: Swiss National Science Foundation (SNSF, grant numbers 200020_132871/1 and 200020_147200; to CP and SM. European Community’s Seventh Framework Program (BrainScaleS, grant no. 269921; to SM; European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 604102 (Human Brain Project; to CP; A grant from the EPFL to the LNMC ( to OH; and Fonds de recherche du Québec - Nature et technologies (FQRNT, to RN. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

 Record created 2015-06-23, last modified 2018-12-03

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