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Recurrent neural network models for working memory of continuous variables: activity manifolds, connectivity patterns, and dynamic codes
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Recurrent neural network models for working memory of continuous variables: activity manifolds, connectivity patterns, and dynamic codes

Christopher J. Cueva
A. Ardalan
M. Tsodyks
N. Qian
ArXiv (abs)PDFHTML

Papers citing "Recurrent neural network models for working memory of continuous variables: activity manifolds, connectivity patterns, and dynamic codes"

4 / 4 papers shown
Title
Back to the Continuous Attractor
Back to the Continuous Attractor
Ábel Ságodi
Guillermo Martín-Sánchez
Piotr Sokól
Il Memming Park
85
3
0
31 Jul 2024
Emergence of functional and structural properties of the head direction system by optimization of recurrent neural networks
Emergence of functional and structural properties of the head direction system by optimization of recurrent neural networks
Christopher J. Cueva
Peter Y. Wang
Matthew Chin
Xue-Xin Wei
55
36
0
21 Dec 2019
Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition
Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition
C. Cadieu
Ha Hong
Daniel L. K. Yamins
Nicolas Pinto
Diego Ardila
E. Solomon
N. Majaj
J. DiCarlo
96
787
0
12 Jun 2014
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Andrew M. Saxe
James L. McClelland
Surya Ganguli
ODL
196
1,852
0
20 Dec 2013

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