A Model of Pattern Separation by Single Neurons - Löffler, Hubert; Gupta, Daya Shankar (Frontiers in Computational Neuroscience, 2022)
Abstract. For efficient processing, spatiotemporal spike patterns representing similar input must be able to be transformed into a less similar output. A new computational model with physiologically plausible parameters shows how the neuronal process referred to as “pattern separation” can be very well achieved by single neurons if the temporal qualities of the output patterns are considered. Spike patterns generated by a varying number of neurons firing with fixed different frequencies within a gamma range are used as input. Temporal and spatial summation of the dendritic input combined with theta-oscillating excitability in the output neuron by Subthreshold Membrane Potential Oscillations (SMOs) lead to high temporal separation by different delays of output spikes of similar input patterns. A Winner Takes All (WTA) mechanism with backward inhibition suffices to transform spatial overlap of the input patterns to much less temporal overlap of the output patterns. The conversion of spatial patterns in input into an output with differently delayed spikes enables high separation effects. Incomplete random connectivity spreads the times up to the first spike across a spatially expanded ensemble of output neurons. With the expansion, random connectivity becomes the spatial distribution mechanism of temporal features. Additionally, a “synfire chain” circuit is proposed to convert temporal differences back into spatial ones.