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Task-based modeling with recurrent neural networks (RNNs) has emerged as a popular way to infer the computational function of different brain regions.
“Gated recurrent units viewed through the lens of continuous time dynamical systems”
Ian. Jordan, Piotr Sokol and Il Park · 1906
Earlier work this paper cites.
“Some methods of speeding up the convergence of iteration methods”
Boris Polyak · 1964
Earlier work this paper cites.
“Introduction to Phase Transitions and Critical Phenomena”
Harry Stanley · 1971
Earlier work this paper cites.
“Neural networks and physical systems with emergent collective computational abilities”
John Hopfield · 1982
Earlier work this paper cites.
“Chaos in random neural networks”
Haim Sompolinsky, Andrea Crisanti and Hans-Jurgen Sommers · 1988
Earlier work this paper cites.
“Relations between two sets of variates”
Harold Hotelling · 1992
Earlier work this paper cites.
“Universality of fully connected recurrent neural networks”
Kenji Doya · 1993
Earlier work this paper cites.
“How the brain keeps the eyes still”
H.. Seung · 1996
Earlier work this paper cites.
“Long short-term memory”
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
“Modern multidimensional scaling: Theory and applications”
Ingwer Borg and Patrick Groenen · 2003
Earlier work this paper cites.
“The Wilson–Cowan model, 36 years later”
Alain Destexhe and Terrence Sejnowski · 2009
Earlier work this paper cites.
“Context-dependent computation by recurrent dynamics in prefrontal cortex” Article
Valerio Mante, David Sussillo, Krishna. Shenoy and William. Newsome · 2013
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Nikolaus Kriegeskorte and Rogier. Kievit · 2013
Earlier work this paper cites.
“From fixed points to chaos: three models of delayed discrimination”
Omri Barak et al · 2013
Earlier work this paper cites.
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Daniel.. Yamins et al · 2014
Earlier work this paper cites.
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Seyed-Mahdi Khaligh-Razavi and Nikolaus Kriegeskorte · 2014
Earlier work this paper cites.
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David Sussillo · 2014
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Later among the works it cites.
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