An efficient analytical reduction of nonlinear detailed neuron models

Detailed conductance-based neuron models, consisting of nonlinear branched dendrites and thousands of synapses, are essential for understanding the integrative and computational properties of single neurons and large neuronal networks, and for interpreting experimental results. Simulations of such models are computationally expensive, severely limiting their utility. We introduce a novel analytic approach to simplify complex nonlinear neuron models while preserving the identity of individual dendrites and synapses. Neuron_Reduce represents each stem dendrite by a unique cylindrical cable, keeping its specific membrane and axial properties. Neuron_Reduce maps synapses and active membrane ion channels to the respective cylinder while preserving their transfer impedance to- and from- the soma as in the detailed model. The reduced model accelerates the simulation speed by up to 200-fold while closely replicating the sub- and supra- threshold voltage dynamics for a variety of cell types and inputs, including the nonlinear "ping pong" interaction between somatic Na + - and dendritic Ca 2+ - spikes, found in L5 neocortical pyramidal cells. Neuron_Reduce also replicates dendritic computations discriminating spatiotemporal input sequences. The reduced neuron models will enable realistic simulations of neural networks at unprecedented scale, including of biologically-inspired "deep networks" and facilitate the construction of neuromorphic-based systems. Neuron_Reduce is publicly available (https://github.com/orena1/neuron_reduce) and is straightforward to implement.