Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 26083597.
- Also identified by DOI 10.1371/journal.pcbi.1004275 and PMC identifier 4470831.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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.
Medical subject headings
- Action Potentials
- Computational Biology
- High-Throughput Screening Assays
- Models, Neurological
- Neurons