A solution to the learning dilemma for recurrent networks of spiking neurons
Bellec et al. present a mathematically founded approximation for gradient descent training of recurrent neural networks without backwards propagation in time. This enables biologically plausible training of spike-based neural network models with working memory and supports on-chip training of neurom...
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Autores principales: | Guillaume Bellec, Franz Scherr, Anand Subramoney, Elias Hajek, Darjan Salaj, Robert Legenstein, Wolfgang Maass |
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Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2020
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Materias: | |
Acceso en línea: | https://doaj.org/article/7910940bc2a3480f8457777933723618 |
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