Computation through neural population dynamics
Significant experimental, computational, and theoretical work has identified rich structure
within the coordinated activity of interconnected neural populations. An emerging challenge …
within the coordinated activity of interconnected neural populations. An emerging challenge …
Dynamics on the manifold: Identifying computational dynamical activity from neural population recordings
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 …
behaviour is fundamental to neuroscience. At the core of this question lie considerations …
[HTML][HTML] Flexible multitask computation in recurrent networks utilizes shared dynamical motifs
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 …
neural networks contextually reconfigure for different computations. In the present work, we …
Auxiliary tasks and exploration enable objectgoal navigation
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 …
navigate to an object instance in an unseen environment. Prior works have shown that end …
Universality and individuality in neural dynamics across large populations of recurrent networks
N Maheswaranathan, A Williams… - Advances in neural …, 2019 - proceedings.neurips.cc
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 …
(RNNs) to infer the computational function of different brain regions. These models are often …
Recurrent neural networks with explicit representation of dynamic latent variables can mimic behavioral patterns in a physical inference task
R Rajalingham, A Piccato, M Jazayeri - Nature Communications, 2022 - nature.com
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 …
hypothesized to rely on establishing mental models of the external world and running mental …
Organizing recurrent network dynamics by task-computation to enable continual learning
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 …
understood how these systems balance the tension between flexibility for learning and …
Bifurcations and loss jumps in RNN training
Recurrent neural networks (RNNs) are popular machine learning tools for modeling and
forecasting sequential data and for inferring dynamical systems (DS) from observed time …
forecasting sequential data and for inferring dynamical systems (DS) from observed time …
The rodent medial prefrontal cortex and associated circuits in orchestrating adaptive behavior under variable demands
Emerging evidence implicates rodent medial prefrontal cortex (mPFC) in tasks requiring
adaptation of behavior to changing information from external and internal sources. However …
adaptation of behavior to changing information from external and internal sources. However …
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics
N Maheswaranathan, A Williams… - Advances in neural …, 2019 - proceedings.neurips.cc
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 …
they are often treated as inscrutable black boxes. Given a trained recurrent network, we …