An efficient analytical reduction of detailed nonlinear neuron models

Realistic simulations of neurons and neural networks are key for understanding neural computations. Here the authors describe Neuron_Reduce, an analytic approach to simplify neurons receiving thousands of synapses and accelerate their simulations by 40–250 folds, while preserving voltage dynamics an...

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Autores principales: Oren Amsalem, Guy Eyal, Noa Rogozinski, Michael Gevaert, Pramod Kumbhar, Felix Schürmann, Idan Segev
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2020
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Acceso en línea:https://doaj.org/article/942319641ed7409a8a1dadc85babcf06
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Sumario:Realistic simulations of neurons and neural networks are key for understanding neural computations. Here the authors describe Neuron_Reduce, an analytic approach to simplify neurons receiving thousands of synapses and accelerate their simulations by 40–250 folds, while preserving voltage dynamics and dendritic computations.