42 citations · 72 across the 3 of their papers we have counts for
3 papers
Exploiting Multiple Timescales in Hierarchical Echo State Networks
Luca Manneschi, Matthew O. A. Ellis, Guido Gigante +3
Echo state networks (ESNs) are a powerful form of reservoir computing that only require training of linear output weights whilst the internal reservoir is formed of fixed randomly…
SpaRCe: Improved Learning of Reservoir Computing Systems through Sparse Representations
Luca Manneschi, Andrew C. Lin, Eleni Vasilaki
"Sparse" neural networks, in which relatively few neurons or connections are active, are common in both machine learning and neuroscience. Whereas in machine learning, "sparsity" i…
Learning sparsity in reservoir computing through a novel bio-inspired algorithm
Luca Manneschi, Andrew C. Lin, Eleni Vasilaki
The mushroom body is the key network for the representation of learned olfactory stimuli in Drosophila and insects. The sparse activity of Kenyon cells, the principal neurons in th…