Revisiting chaos in stimulus-driven spiking networks: signal encoding and discrimination

TL;DR

This paper demonstrates that highly chaotic recurrent spiking neural networks can reliably encode and discriminate temporal stimuli, challenging the notion that chaos impairs precise information processing.

Contribution

It reveals that chaos in spiking networks can support accurate stimulus encoding and discrimination, highlighting a low-dimensional chaos structure that preserves stimulus information.

Findings

Chaotic networks produce reliable spike patterns for stimulus encoding.

Small groups of neurons can encode significant information about stimuli.

Chaos does not prevent, and may even facilitate, precise stimulus discrimination.

Abstract

Highly connected recurrent neural networks often produce chaotic dynamics, meaning their precise activity is sensitive to small perturbations. What are the consequences for how such networks encode streams of temporal stimuli? On the one hand, chaos is a strong source of randomness, suggesting that small changes in stimuli will be obscured by intrinsically generated variability. On the other hand, recent work shows that the type of chaos that occurs in spiking networks can have a surprisingly low-dimensional structure, suggesting that there may be "room" for fine stimulus features to be precisely resolved. Here we show that strongly chaotic networks produce patterned spikes that reliably encode time-dependent stimuli: using a decoder sensitive to spike times on timescales of 10's of ms, one can easily distinguish responses to very similar inputs. Moreover, recurrence serves to distribute signals throughout chaotic networks so that small groups of cells can encode substantial information about signals arriving elsewhere. A conclusion is that the presence of strong chaos in recurrent networks does not prohibit precise stimulus encoding.