A Computational Model of Representation Learning in the Brain Cortex, Integrating Unsupervised and Reinforcement Learning

TL;DR

This paper presents a computational model suggesting that the brain's cortex uses a combination of unsupervised and reinforcement learning to acquire sensory and motor representations, improving task performance.

Contribution

It introduces a novel integrated model of unsupervised and reinforcement learning in the cortex, supported by simulations on a visual categorization task.

Findings

Balanced learning improves task performance

Excessive unsupervised learning under-represents relevant features

Excessive reinforcement learning causes slow learning and local minima

Abstract

A common view on the brain learning processes proposes that the three classic learning paradigms -- unsupervised, reinforcement, and supervised -- take place in respectively the cortex, the basal-ganglia, and the cerebellum. However, dopamine outbursts, usually assumed to encode reward, are not limited to the basal ganglia but also reach prefrontal, motor, and higher sensory cortices. We propose that in the cortex the same reward-based trial-and-error processes might support not only the acquisition of motor representations but also of sensory representations. In particular, reward signals might guide trial-and-error processes that mix with associative learning processes to support the acquisition of representations better serving downstream action selection. We tested the soundness of this hypothesis with a computational model that integrates unsupervised learning (Contrastive Divergence) and reinforcement learning (REINFORCE). The model was tested with a task requiring different responses to different visual images grouped in categories involving either colour, shape, or size. Results show that a balanced mix of unsupervised and reinforcement learning processes leads to the best performance. Indeed, excessive unsupervised learning tends to under-represent task-relevant features while excessive reinforcement learning tends to initially learn slowly and then to incur in local minima. These results stimulate future empirical studies on category learning directed to investigate similar effects in the extrastriate visual cortices. Moreover, they prompt further computational investigations directed to study the possible advantages of integrating unsupervised and reinforcement learning processes.