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Causal Influences Decouple From Their Underlying Network Structure In Echo State Networks. (arXiv:2205.11947v1 [cs.LG])
May 25, 2022, 1:10 a.m. | Kayson Fakhar, Fatemeh Hadaeghi, Claus C. Hilgetag
cs.LG updates on arXiv.org arxiv.org
Echo State Networks (ESN) are versatile recurrent neural network models in
which the hidden layer remains unaltered during training. Interactions among
nodes of this static backbone produce diverse representations of the given
stimuli that are harnessed by a read-out mechanism to perform computations
needed for solving a given task. ESNs are accessible models of neuronal
circuits, since they are relatively inexpensive to train. Therefore, ESNs have
become attractive for neuroscientists studying the relationship between neural
structure, function, and behavior. For …
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