Tag: supervised learning

Talk at MPI Göttingen on June 28th

Thrilled to share the latest results on learning in multi-layer spiking networks using biologically plausible surrogate gradients at the “Third workshop on advanced methods in theoretical neuroscience” at the Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany. Thanks to

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SuperSpike: Supervised learning in spiking neural networks — paper and code published

I am happy to announce that the SuperSpike paper and code are finally published. Here is an example of a network with one hidden layer which is learning to produce a Radcliffe Camera spike train from frozen Poisson input spike

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Supervised learning in multi-layer spiking neural networks

We just put a conference paper version of “SuperSpike”, our work on supervised learning in multi-layer spiking neural networks to the arXiv https://arxiv.org/abs/1705.11146. As always I am keen to get your feedback.

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