An efficient analytical reduction of detailed nonlinear neuron models

Oren Amsalem (), Guy Eyal, Noa Rogozinski, Michael Gevaert, Pramod Kumbhar, Felix Schürmann and Idan Segev
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Oren Amsalem: Hebrew University of Jerusalem
Guy Eyal: Hebrew University of Jerusalem
Noa Rogozinski: Hebrew University of Jerusalem
Michael Gevaert: École polytechnique fédérale de Lausanne (EPFL)
Pramod Kumbhar: École polytechnique fédérale de Lausanne (EPFL)
Felix Schürmann: École polytechnique fédérale de Lausanne (EPFL)
Idan Segev: Hebrew University of Jerusalem

Nature Communications, 2020, vol. 11, issue 1, 1-13

Abstract: Abstract Detailed conductance-based nonlinear neuron models consisting of thousands of synapses are key for understanding of the computational properties of single neurons and large neuronal networks, and for interpreting experimental results. Simulations of these models are computationally expensive, considerably curtailing their utility. Neuron_Reduce is a new analytical approach to reduce the morphological complexity and computational time of nonlinear neuron models. Synapses and active membrane channels are mapped to the reduced model preserving their transfer impedance to the soma; synapses with identical transfer impedance are merged into one NEURON process still retaining their individual activation times. Neuron_Reduce accelerates the simulations by 40–250 folds for a variety of cell types and realistic number (10,000–100,000) of synapses while closely replicating voltage dynamics and specific dendritic computations. The reduced neuron-models will enable realistic simulations of neural networks at unprecedented scale, including networks emerging from micro-connectomics efforts and biologically-inspired “deep networks”. Neuron_Reduce is publicly available and is straightforward to implement.

Date: 2020
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DOI: 10.1038/s41467-019-13932-6

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