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Found 5 papers, 0 papers with code
Transitions in echo index and dependence on input repetitions
Casting our theoretical findings in the RNN computing framework, we obtain that for small amplitude forcing the echo index corresponds to the number of attractors for the input-free system, while for large amplitude forcing, the echo index reduces to one.
Edge of stability echo state networks
With the goal of bringing together the fading memory property and the ability to retain as much memory as possible, in this paper we introduce a new ESN architecture, called the Edge of Stability Echo State Network (ES$^2$N).
Random orthogonal additive filters: a solution to the vanishing/exploding gradient of deep neural networks
Since the recognition in the early nineties of the vanishing/exploding (V/E) gradient issue plaguing the training of neural networks (NNs), significant efforts have been exerted to overcome this obstacle.
Cross frequency coupling in next generation inhibitory neural mass models
Coupling among neural rhythms is one of the most important mechanisms at the basis of cognitive processes in the brain.
Adaptation and Self-Organizing Systems Disordered Systems and Neural Networks
Interpreting recurrent neural networks behaviour via excitable network attractors
Simulations conducted on a controlled benchmark task confirm the relevance of these attractors for interpreting the behaviour of recurrent neural networks, at least for tasks that involve learning a finite number of stable states and transitions between them.
