The thermodynamic temperature of a rhythmic spiking network

Abstract

Artificial neural networks built from two-state neurons are powerful computational substrates, whose computational ability is well understood by analogy with statistical mechanics. In this work, we introduce similar analogies in the context of spiking neurons in a fixed time window, where excitatory and inhibitory inputs drawn from a Poisson distribution play the role of temperature. For single neurons with a "bandgap" between their inputs and the spike threshold, this temperature allows for stochastic spiking. By imposing a global inhibitory rhythm over the fixed time windows, we connect neurons into a network that exhibits synchronous, clock-like updating akin to neural networks. We implement a single-layer Boltzmann machine without learning to demonstrate our model.

Neural Sampling by Irregular Gating Inhibition of Spiking Neurons and Attractor Networks

“…It has been argued that the dynamics and spiking behavior of neurons can be interpreted as an expression of a sampling process by means of which the brain performs such inference operations [7,2]. This approach is appealing because it allows drawing parallels between neural dynamics and wellestablished algorithms such as Markov-Chain Monte-Carlo (MCMC) sampling [5,20,13] or restricted Boltzmann-Machines (RBM) [12,17,22], and various constraint satisfaction problem (CSP) solvers [10,15].…”