Nature Communications (Jan 2020)

An efficient analytical reduction of detailed nonlinear neuron models

  • Oren Amsalem,
  • Guy Eyal,
  • Noa Rogozinski,
  • Michael Gevaert,
  • Pramod Kumbhar,
  • Felix Schürmann,
  • Idan Segev

DOI
https://doi.org/10.1038/s41467-019-13932-6
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 13

Abstract

Read online

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.