Graded, Dynamically Routable Information Processing with Synfire-Gated Synfire Chains

TL;DR

This paper introduces synfire-gated synfire chains (SGSCs), a neural circuit model that enables rapid, graded, and robust information transfer and processing using pulse-gated chains, advancing understanding of coherent neural activity.

Contribution

The paper presents SGSCs, a novel neural circuit architecture that propagates graded information efficiently and robustly, with demonstrated computational capabilities for decision-making tasks.

Findings

SGSCs enable rapid cascade of graded information in neural circuits.

SGSCs are robust against variability in population size, timing, and synaptic strength.

Demonstrated a spike-based neural circuit that processes input, makes decisions, and self-terminates.

Abstract

Coherent neural spiking and local field potentials are believed to be signatures of the binding and transfer of information in the brain. Coherent activity has now been measured experimentally in many regions of mammalian cortex. Synfire chains are one of the main theoretical constructs that have been appealed to to describe coherent spiking phenomena. However, for some time, it has been known that synchronous activity in feedforward networks asymptotically either approaches an attractor with fixed waveform and amplitude, or fails to propagate. This has limited their ability to explain graded neuronal responses. Recently, we have shown that pulse-gated synfire chains are capable of propagating graded information coded in mean population current or firing rate amplitudes. In particular, we showed that it is possible to use one synfire chain to provide gating pulses and a second, pulse-gated synfire chain to propagate graded information. We called these circuits synfire-gated synfire chains (SGSCs). Here, we present SGSCs in which graded information can rapidly cascade through a neural circuit, and show a correspondence between this type of transfer and a mean-field model in which gating pulses overlap in time. We show that SGSCs are robust in the presence of variability in population size, pulse timing and synaptic strength. Finally, we demonstrate the computational capabilities of SGSC-based information coding by implementing a self-contained, spike-based, modular neural circuit that is triggered by, then reads in streaming input, processes the input, then makes a decision based on the processed information and shuts itself down.