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Determination of the Edge of Criticality in Echo State Networks Through Fisher Information Maximization
2018
IEEE Transactions on Neural Networks and Learning Systems
It is a widely accepted fact that the computational capability of recurrent neural networks is maximized on the so-called "edge of criticality". Once the network operates in this configuration, it performs efficiently on a specific application both in terms of (i) low prediction error and (ii) high short-term memory capacity. Since the behavior of recurrent networks is strongly influenced by the particular input signal driving the dynamics, a universal, application-independent method for
doi:10.1109/tnnls.2016.2644268
pmid:28092580
fatcat:4tgpqjv5o5fwti4dpzhg53tqq4