Optimal localist and distributed coding of spatiotemporal spike patterns through STDP and coincidence detection

TL;DR

This paper analyzes how single neurons can optimally detect multiple spatiotemporal spike patterns using STDP and coincidence detection, demonstrating the potential for both localist and distributed coding schemes.

Contribution

It extends previous theory to multi-pattern detection, showing that STDP enables neurons to learn multiple patterns and maintain high SNR with many patterns.

Findings

SNR decreases slowly with increasing patterns, remaining acceptable for tens of patterns.

STDP allows neurons to become selective to multiple patterns without supervision.

Neurons can learn multiple patterns with low thresholds, supporting distributed coding.

Abstract

Repeating spatiotemporal spike patterns exist and carry information. Here we investigated how a single spiking neuron can optimally respond to one given pattern (localist coding), or to either one of several patterns (distributed coding, i.e. the neuron's response is ambiguous but the identity of the pattern could be inferred from the response of multiple neurons), but not to random inputs. To do so, we extended a theory developed in a previous paper [Masquelier, 2017], which was limited to localist coding. More specifically, we computed analytically the signal-to-noise ratio (SNR) of a multi-pattern-detector neuron, using a threshold-free leaky integrate-and-fire (LIF) neuron model with non-plastic unitary synapses and homogeneous Poisson inputs. Surprisingly, when increasing the number of patterns, the SNR decreases slowly, and remains acceptable for several tens of independent patterns. In addition, we investigated whether spike-timing-dependent plasticity (STDP) could enable a neuron to reach the theoretical optimal SNR. To this aim, we simulated a LIF equipped with STDP, and repeatedly exposed it to multiple input spike patterns, embedded in equally dense Poisson spike trains. The LIF progressively became selective to every repeating pattern with no supervision, and stopped discharging during the Poisson spike trains. Furthermore, using certain STDP parameters, the resulting pattern detectors were optimal. Tens of independent patterns could be learned by a single neuron using a low adaptive threshold, in contrast with previous studies, in which higher thresholds led to localist coding only. Taken together these results suggest that coincidence detection and STDP are powerful mechanisms, fully compatible with distributed coding. Yet we acknowledge that our theory is limited to single neurons, and thus also applies to feed-forward networks, but not to recurrent ones.