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An efficient analytical reduction of detailed nonlinear neuron models

Authors: Oren Amsalem1, Guy Eyal1, Noa Rogozinski1, Michael Gevaert 3, Pramod Kumbhar3, Felix Schürmann3, Idan Segev1,2

Author information: 1Department of Neurobiology, 2Edmond and Lily Safra Center for Brain Sciences, the Hebrew University of Jerusalem, Israel 3 Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland

Corresponding authors: Oren Amsalem ( )

Journal: Nature Communications

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Citation: Amsalem, O., Eyal, G., Rogozinski, N., Gevaert, M., Kumbhar, P., Schürmann, F., and Segev, I. (2020). An efficient analytical reduction of detailed nonlinear neuron models. Nat. Commun. 2020 111 11, 1–13.



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 fold 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.

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Neuron Reduce

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Detailed instruction and source code is available on GitHub: