Bayesian modelling of visual discrimination learning in mice

TL;DR

This paper introduces a Bayesian framework to model how mice learn visual discrimination tasks, linking neural activity, internal representations, and behavior during learning.

Contribution

It presents a novel Bayesian modeling approach that captures learning dynamics and internal representations in mice during visual discrimination tasks.

Findings

Model successfully infers internal representations used during learning

Framework allows comparison of different hypotheses about neural processing

Provides a Markov model for behavior generation during learning

Abstract

The brain constantly turns large flows of sensory information into selective representations of the environment. It, therefore, needs to learn to process those sensory inputs that are most relevant for behaviour. It is not well understood how learning changes neural circuits in visual and decision-making brain areas to adjust and improve its visually guided decision-making. To address this question, head-fixed mice were trained to move through virtual reality environments and learn visual discrimination while neural activity was recorded with two-photon calcium imaging. Previously, descriptive models of neuronal activity were fitted to the data, which was used to compare the activity of excitatory and different inhibitory cell types. However, the previous models did not take the internal representations and learning dynamics into account. Here, I present a framework to infer a model of internal representations that are used to generate the behaviour during the task. We model the learning process from untrained mice to trained mice within the normative framework of the ideal Bayesian observer and provide a Markov model for generating the movement and licking. The framework provides a space of models where a range of hypotheses about the internal representations could be compared for a given data set.