Rate-space attractors and low dimensional dynamics interact with spike-synchrony statistics in neural networks

Abstract

Mechanistic models of cognitive phenomena often make use of neural networks, which allow researchers to examine relationships between neurobiology and the computations suspected to underlie cognition. These models typically make use of neural firing rates, as do analyses of in-vivo data, with the dimension of neural dynamics receiving special attention. Treating time-binned spiking activity as a sequence of binary vectors (spike-words) should prove complementary to rate-space analyses, and has been shown to provide links with statistical physics. We investigate the interaction between these two analyses using theory and simulations to show how signatures of rate-dynamics are found in spike-word distributions. We find that a global integration over the eigenvalues of linear dynamics local to attracting subspaces can modify spike-synchrony, and we quantify how this impacts informational and thermodynamic properties of these systems. The research outlined here will have implications for the interpretation of neural data, the use of population codes for tasks such as Bayesian inference, and for various resource rational models attempting to bridge the gap between computation and implementation.