Attractor and integrator networks in the brain
In this Review, we describe the singular success of attractor neural network models in
describing how the brain maintains persistent activity states for working memory, corrects …
describing how the brain maintains persistent activity states for working memory, corrects …
Systems neuroscience of natural behaviors in rodents
Animals evolved in complex environments, producing a wide range of behaviors, including
navigation, foraging, prey capture, and conspecific interactions, which vary over timescales …
navigation, foraging, prey capture, and conspecific interactions, which vary over timescales …
No free lunch from deep learning in neuroscience: A case study through models of the entorhinal-hippocampal circuit
Research in Neuroscience, as in many scientific disciplines, is undergoing a renaissance
based on deep learning. Unique to Neuroscience, deep learning models can be used not …
based on deep learning. Unique to Neuroscience, deep learning models can be used not …
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems
J Smith, S Linderman… - Advances in Neural …, 2021 - proceedings.neurips.cc
Recurrent neural networks (RNNs) are powerful models for processing time-series data, but
it remains challenging to understand how they function. Improving this understanding is of …
it remains challenging to understand how they function. Improving this understanding is of …
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 …
Coding of latent variables in sensory, parietal, and frontal cortices during closed-loop virtual navigation
We do not understand how neural nodes operate and coordinate within the recurrent action-
perception loops that characterize naturalistic self-environment interactions. Here, we record …
perception loops that characterize naturalistic self-environment interactions. Here, we record …
Place-cell capacity and volatility with grid-like inputs
What factors constrain the arrangement of the multiple fields of a place cell? By modeling
place cells as perceptrons that act on multiscale periodic grid-cell inputs, we analytically …
place cells as perceptrons that act on multiscale periodic grid-cell inputs, we analytically …
Position: Maximizing Neural Regression Scores May Not Identify Good Models of the Brain
R Schaeffer, M Khona, S Chandra… - UniReps: 2nd Edition …, 2024 - openreview.net
A prominent methodology in computational neuroscience posits that the brain can be
understood by identifying which artificial neural network models most accurately predict …
understood by identifying which artificial neural network models most accurately predict …
Trajectory Tracking via Multiscale Continuous Attractor Networks
Animals and insects showcase remarkably robust and adept navigational abilities, up to
literally circumnavigating the globe. Primary progress in robotics inspired by these natural …
literally circumnavigating the globe. Primary progress in robotics inspired by these natural …
[HTML][HTML] Spatial uncertainty and environmental geometry in navigation
Variations in the geometry of the environment, such as the shape and size of an enclosure,
have profound effects on navigational behavior and its neural underpinning. Here, we show …
have profound effects on navigational behavior and its neural underpinning. Here, we show …