Topic: attention mechanism

Deep learning models have shown significant efficacy in predicting neural activity, offering promising avenues for understanding complex neural dynamics. However, disentangling key explanatory factors---crucial for achieving generalizable and interpretable neural activity predictions---remains underexplored. Such disentanglement is essential for downstream simulations and for elucidating inter-individual variability in neural responses. To address this gap, we introduce the Query-based Neural Activity Prediction (qNAP) model, a novel framework that leverages an attention mechanism to explicitl

Neuromodulatory systems play a crucial role in the brain, driving rapid, global changes in neuronal computation and learning. While the dopaminergic system is thought to broadcast a scalar reward prediction error, the serotonin system’s function has been difficult to elucidate, partly due to the heterogeneity of its individual neuronal responses [1]. Although serotonergic neurons are known to respond to various salient events [2] and rewards [3], which can be accounted for by ad-hoc extensions to a reinforcement learning framework, our study presents the first comprehensive, normative framewor