Coin-Flipping In The Brain: Statistical Learning with Neuronal Assemblies

TL;DR

This paper demonstrates that a biologically plausible neural model can learn statistical patterns and make probabilistic decisions by harnessing neural noise, supporting the idea that brain noise aids cognition.

Contribution

The study introduces NEMO, a neural model showing how noise and assembly connections enable probabilistic learning and internal model formation.

Findings

Connections between assemblies encode statistical information.

Ambient noise facilitates probabilistic choices.

NEMO can learn Markov chains from stimulus sequences.

Abstract

How intelligence arises from the brain is a central problem in science. A crucial aspect of intelligence is dealing with uncertainty -- developing good predictions about one's environment, and converting these predictions into decisions. The brain itself seems to be noisy at many levels, from chemical processes which drive development and neuronal activity to trial variability of responses to stimuli. One hypothesis is that the noise inherent to the brain's mechanisms is used to sample from a model of the world and generate predictions. To test this hypothesis, we study the emergence of statistical learning in NEMO, a biologically plausible computational model of the brain based on stylized neurons and synapses, plasticity, and inhibition, and giving rise to assemblies -- a group of neurons whose coordinated firing is tantamount to recalling a location, concept, memory, or other primitive item of cognition. We show in theory and simulation that connections between assemblies record statistics, and ambient noise can be harnessed to make probabilistic choices between assemblies. This allows NEMO to create internal models such as Markov chains entirely from the presentation of sequences of stimuli. Our results provide a foundation for biologically plausible probabilistic computation, and add theoretical support to the hypothesis that noise is a useful component of the brain's mechanism for cognition.