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 …
Extracting computational mechanisms from neural data using low-rank RNNs
An influential framework within systems neuroscience posits that neural computations can
be understood in terms of low-dimensional dynamics in recurrent circuits. A number of …
be understood in terms of low-dimensional dynamics in recurrent circuits. A number of …
Abstract representations emerge naturally in neural networks trained to perform multiple tasks
WJ Johnston, S Fusi - Nature Communications, 2023 - nature.com
Humans and other animals demonstrate a remarkable ability to generalize knowledge
across distinct contexts and objects during natural behavior. We posit that this ability to …
across distinct contexts and objects during natural behavior. We posit that this ability to …
Harnessing behavioral diversity to understand neural computations for cognition
Highlights•Behavioral diversity can be leveraged to reveal neural circuit functions.•Complex
tasks allow inference of latent behavioral variables to relate to neural activity.•Movements …
tasks allow inference of latent behavioral variables to relate to neural activity.•Movements …
Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice
We study how recurrent neural networks (RNNs) solve a hierarchical inference task
involving two latent variables and disparate timescales separated by 1-2 orders of …
involving two latent variables and disparate timescales separated by 1-2 orders of …
Unsupervised neural network models of the ventral visual stream
Deep neural networks currently provide the best quantitative models of the response
patterns of neurons throughout the primate ventral visual stream. However, such networks …
patterns of neurons throughout the primate ventral visual stream. However, such networks …
A neural network trained for prediction mimics diverse features of biological neurons and perception
Recent work has shown that convolutional neural networks (CNNs) trained on image
recognition tasks can serve as valuable models for predicting neural responses in primate …
recognition tasks can serve as valuable models for predicting neural responses in primate …
Bayesian computation through cortical latent dynamics
Statistical regularities in the environment create prior beliefs that we rely on to optimize our
behavior when sensory information is uncertain. Bayesian theory formalizes how prior …
behavior when sensory information is uncertain. Bayesian theory formalizes how prior …
Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks
T Miconi - Elife, 2017 - elifesciences.org
Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-
relevant variables. Chaotic recurrent networks, which spontaneously generate rich …
relevant variables. Chaotic recurrent networks, which spontaneously generate rich …
[HTML][HTML] Towards a foundation model of the mouse visual cortex
Understanding the brain's perception algorithm is a highly intricate problem, as the inherent
complexity of sensory inputs and the brain's nonlinear processing make characterizing …
complexity of sensory inputs and the brain's nonlinear processing make characterizing …