We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models.
- Authors
Pozzorini, Christian; Mensi, Skander; Hagens, Olivier; Naud, Richard; Koch, Christof; Gerstner, Wulfram
- 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.
- Subjects
PATCH-clamp techniques (Electrophysiology); NEURAL circuitry; ELECTROPHYSIOLOGY; COMPUTER simulation; BIOPHYSICS; MATHEMATICAL models
- Publication
PLoS Computational Biology, 2015, Vol 11, Issue 6, p1
- ISSN
1553-734X
- Publication type
Article
- DOI
10.1371/journal.pcbi.1004275