Millidge, B. G. | bioRxiv Science

bioRxiv2023

Reward-Bases: Dopaminergic Mechanisms for Adaptive Acquisition of Multiple Reward Types

Animals can adapt their preferences for different types for reward according to physiological state, such as hunger or thirst. To describe this ability, we propose a simple extension of temporal difference model that learns multiple values of each state according to different reward dimensions such as food or water. By weighting these learned values according to the current needs, behaviour may be flexibly adapted to present demands. Our model predicts that different dopamine neurons should be selective for different reward dimensions. We reanalysed data from primate dopamine neurons and observed that in addition to subjective value, dopamine neurons encode a gradient of reward dimensions; some neurons respond most to food rewards while the others respond more to fluids. Moreover, our model reproduces instant generalization to new physiological state seen in dopamine responses and in behaviour. Our results demonstrate how simple neural circuit can flexibly optimize behaviour according to animals needs.

neuroscience↗

bioRxiv2022

Inferring Neural Activity Before Plasticity: A Foundation for Learning Beyond Backpropagation

For both humans and machines, the essence of learning is to pinpoint which components in its information processing pipeline are responsible for an error in its output -- a challenge that is known as credit assignment. How the brain solves credit assignment is a key question in neuroscience, and also of significant importance for artificial intelligence. It has long been assumed that credit assignment is best solved by backpropagation, which is also the foundation of modern machine learning. However, it has been questioned whether it is possible for the brain to implement backpropagation and learning in the brain may actually be more efficient and effective than backpropagation. Here, we set out a fundamentally different principle on credit assignment, called prospective configuration. In prospective configuration, the network first infers the pattern of neural activity that should result from learning, and then the synaptic weights are modified to consolidate the change in neural activity. We demonstrate that this distinct mechanism, in contrast to backpropagation, (1) underlies learning in a well-established family of models of cortical circuits, (2) enables learning that is more efficient and effective in many contexts faced by biological organisms, and (3) reproduces surprising patterns of neural activity and behaviour observed in diverse human and animal learning experiments. Our findings establish a new foundation for learning beyond backpropagation, for both understanding biological learning and building artificial intelligence.

neuroscience↗