Neural mechanisms of adaptive behavior
Animals and humans rapidly adapt their behavior to dynamic environmental changes, such as predator threats or fluctuating food resources, often without immediate rewards. Existing literature posits that animals rely on internal representations of the environment, termed “beliefs”, for their decision policy. However, previous work ties belief updates to external reward signals, which does not explain adaptation in scenarios where trial-and-error approaches are inefficient or potentially perilous. In this work, we propose that the brain utilize dynamic representations that continuously infer the state of the environment, allowing it to update behavior rapidly. I will present a Bayesian theory for state inference in a partially observed Markov Decision Process with multiple interacting latent variables. Optimal behavior requires knowledge of hidden interactions between latent states. I will show that recurrent neural networks trained through reinforcement solve the task by learning the hidden interaction between latent states, and their activity encodes the dynamics of the optimal Bayesian estimators. The behavior of rodents trained on an identical task aligns with our theoretical model and neural network simulations, suggesting that the brain utilizes dynamic internal state representation and inference. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2024-01-31. Recording duration: 00:51:21.
The role of the primate prefrontal cortex in inferring the state of the world and predicting change
In an ever-changing environment, uncertainty is omnipresent. To deal with this, organisms have evolved mechanisms that allow them to take advantage of environmental regularities in order to make decisions robustly and adjust their behavior efficiently, thus maximizing their chances of survival. In this talk, I will present behavioral evidence that animals perform model-based state inference to predict environmental state changes and adjust their behavior rapidly, rather than slowly updating choice values. This model-based inference process can be described using Bayesian change-point models. Furthermore, I will show that neural populations in the prefrontal cortex accurately predict behavioral switches, and that the activity of these populations is associated with Bayesian estimates. In addition, we will see that learning leads to the emergence of a high-dimensional representational subspace that can be reused when the animals re-learn a previously learned set of action-value associations. Altogether, these findings highlight the role of the PFC in representing a belief about the current state of the world.