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A solution to the learning dilemma for recurrent networks of spiking neurons

Guillaume Bellec, Franz Scherr, Anand Subramoney, Elias Hajek, Darjan Salaj, Robert Legenstein, Wolfgang Maass
doi: https://doi.org/10.1101/738385
Guillaume Bellec
1Institute of Theoretical Computer Science, Graz University of Technology, Inffeldgasse 16b, Graz, Austria
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Franz Scherr
1Institute of Theoretical Computer Science, Graz University of Technology, Inffeldgasse 16b, Graz, Austria
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Anand Subramoney
1Institute of Theoretical Computer Science, Graz University of Technology, Inffeldgasse 16b, Graz, Austria
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Elias Hajek
1Institute of Theoretical Computer Science, Graz University of Technology, Inffeldgasse 16b, Graz, Austria
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Darjan Salaj
1Institute of Theoretical Computer Science, Graz University of Technology, Inffeldgasse 16b, Graz, Austria
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Robert Legenstein
1Institute of Theoretical Computer Science, Graz University of Technology, Inffeldgasse 16b, Graz, Austria
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Wolfgang Maass
1Institute of Theoretical Computer Science, Graz University of Technology, Inffeldgasse 16b, Graz, Austria
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  • For correspondence: maass@igi.tugraz.at
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Abstract

Recurrently connected networks of spiking neurons underlie the astounding information processing capabilities of the brain. But in spite of extensive research, it has remained open how learning through synaptic plasticity could be organized in such networks. We argue that two pieces of this puzzle were provided by experimental data from neuroscience. A new mathematical insight tells us how they need to be combined to enable network learning through gradient descent. The resulting learning method – called e-prop – approaches the performance of BPTT (backpropagation through time), the best known method for training recurrent neural networks in machine learning. But in contrast to BPTT, e-prop is biologically plausible. In addition, it elucidates how brain-inspired new computer chips – that are drastically more energy efficient – can be enabled to learn.

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Posted August 19, 2019.
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A solution to the learning dilemma for recurrent networks of spiking neurons
Guillaume Bellec, Franz Scherr, Anand Subramoney, Elias Hajek, Darjan Salaj, Robert Legenstein, Wolfgang Maass
bioRxiv 738385; doi: https://doi.org/10.1101/738385
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A solution to the learning dilemma for recurrent networks of spiking neurons
Guillaume Bellec, Franz Scherr, Anand Subramoney, Elias Hajek, Darjan Salaj, Robert Legenstein, Wolfgang Maass
bioRxiv 738385; doi: https://doi.org/10.1101/738385

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