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Short-Term Memory in Orthogonal Neural Networks

2004-02-17
Olivia L. White, Daniel D. Lee, Haim Sompolinsky

Abstract

We study the ability of linear recurrent networks obeying discrete time dynamics to store long temporal sequences that are retrievable from the instantaneous state of the network. We calculate this temporal memory capacity for both distributed shift register and random orthogonal connectivity matrices. We show that the memory capacity of these networks scales with system size.

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URL

https://arxiv.org/abs/cond-mat/0402452

PDF

https://arxiv.org/pdf/cond-mat/0402452


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