Dissecting the Complexities of Learning With Infinite Hidden Markov Models

作者：Sebastian A. Bruijns, Kcénia Bougrova, Inês C. Laranjeira, Petrina Lau, Guido T. Meijer, Nathaniel J Miska, Jean‐Paul Noel, Alejandro Pan-Vazquez, Noam Roth, Karolina Socha, Anne E Urai, Peter Dayan · 发表于：bioRxiv (Cold Spring Harbor Laboratory) · 年份：2023 · DOI：10.1101/2023.12.22.573001 · 被引用次数：11 · 研究领域：Neural dynamics and brain function、Memory and Neural Mechanisms、Neuroendocrine regulation and behavior

Abstract Learning to exploit the contingencies of a complex experiment is not an easy task for animals. Individuals learn in an idiosyncratic manner, revising their approaches multiple times as they are shaped, or shape themselves, and potentially end up with different strategies. Their long-run learning curves are therefore a tantalizing target for the sort of individualized quantitative characterizations that sophisticated modelling can provide. However, any such model requires a flexible and extensible structure which can capture radically new behaviours as well as slow changes in existing ones. To this end, we suggest a dynamic input-output infinite hidden semi-Markov model, whose latent states are associated with specific components of behaviour. This model includes an infinite number of potential states and so has the capacity to describe substantially new behaviours by unearthing extra states; while dynamics in the model allow it to capture more modest adaptations to existing behaviours. We individually fit the model to data collected from more than 100 mice as they learned a contrast detection task over tens of sessions and around fifteen thousand trials each. Despite large individual differences, we found that most animals progressed through three major stages of learning, the transitions between which were marked by distinct additions to task understanding. We furthermore showed that marked changes in behaviour are much more likely to occur at the very beginning of ...