Cortical oscillations implement a backbone for sampling-based computation in spiking neural networks

TL;DR

This paper suggests cortical oscillations facilitate sampling-based probabilistic inference in the brain by acting as a form of simulated tempering, thereby helping to solve the mixing problem in neural representations.

Contribution

It introduces a novel computational role for cortical oscillations as a mechanism for sampling-based inference, supported by mathematical analysis and computer simulations.

Findings

Oscillations modulate exploration in neural sampling.

Cortical rhythms act as a form of simulated tempering.

Implications for understanding brain functions like memory and perception.

Abstract

Being permanently confronted with an uncertain world, brains have faced evolutionary pressure to represent this uncertainty in order to respond appropriately. Often, this requires visiting multiple interpretations of the available information or multiple solutions to an encountered problem. This gives rise to the so-called mixing problem: since all of these "valid" states represent powerful attractors, but between themselves can be very dissimilar, switching between such states can be difficult. We propose that cortical oscillations can be effectively used to overcome this challenge. By acting as an effective temperature, background spiking activity modulates exploration. Rhythmic changes induced by cortical oscillations can then be interpreted as a form of simulated tempering. We provide a rigorous mathematical discussion of this link and study some of its phenomenological implications in computer simulations. This identifies a new computational role of cortical oscillations and connects them to various phenomena in the brain, such as sampling-based probabilistic inference, memory replay, multisensory cue combination, and place cell flickering.