Significant experimental, computational, and theoretical work has identified rich structure
within the coordinated activity of interconnected neural populations. An emerging challenge …

The question of how the collective activity of neural populations gives rise to complex
behaviour is fundamental to neuroscience. At the core of this question lie considerations …

Flexible computation is a hallmark of intelligent behavior. However, little is known about how
neural networks contextually reconfigure for different computations. In the present work, we …

Abstract ObjectGoal Navigation (ObjectNav) is an embodied task wherein agents are to
navigate to an object instance in an unseen environment. Prior works have shown that end …

Many recent studies have employed task-based modeling with recurrent neural networks
(RNNs) to infer the computational function of different brain regions. These models are often …

Primates can richly parse sensory inputs to infer latent information. This ability is
hypothesized to rely on establishing mental models of the external world and running mental …

Recurrent neural networks (RNNs) are popular machine learning tools for modeling and
forecasting sequential data and for inferring dynamical systems (DS) from observed time …

Biological systems face dynamic environments that require continual learning. It is not well
understood how these systems balance the tension between flexibility for learning and …

Emerging evidence implicates rodent medial prefrontal cortex (mPFC) in tasks requiring
adaptation of behavior to changing information from external and internal sources. However …

Recurrent neural networks (RNNs) are a widely used tool for modeling sequential data, yet
they are often treated as inscrutable black boxes. Given a trained recurrent network, we …