DOI: 10.1101/506485Dec 27, 2018Paper

An efficient analytical reduction of detailed nonlinear neuron models

BioRxiv : the Preprint Server for Biology
Oren AmsalemIdan Segev

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.

Related Concepts

Inspiration Function
Ion Channel
Neurons
Stem Cells
Synapses
Electric Impedance
Neocortex
Branching (Qualifier Value)
Membrane
Sodium Cation

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