Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models.
PLoS Comput Biol
; 11(6): e1004275, 2015 Jun.
Article
em En
| MEDLINE
| ID: mdl-26083597
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.
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Base de dados:
MEDLINE
Assunto principal:
Potenciais de Ação
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Biologia Computacional
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Ensaios de Triagem em Larga Escala
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Modelos Neurológicos
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Neurônios
Idioma:
En
Ano de publicação:
2015
Tipo de documento:
Article