I am eagerly anticipating fun discussions at Cosyne 2019. We have a poster at the main meeting and I will give two talks at the workshops. If biological learning and spiking neural networks tickle your fancy, come along. I will post some details and supplementary material below.
Thursday, 28 February 2019, 8.30p — Poster Session 1
I-98 Rapid spatiotemporal coding in trained multi-layer and recurrent spiking neural networks. Friedemann Zenke, Tim P Vogels
Code: Surrogate gradients in PyTorch
Please try this at home! I have put the early beginnings of a tutorial on how to train simple spiking networks with surrogate gradients using PyTorch here:
Talks at the workshops
9:25-9:50 on Monday 4th of March 2019 in the workshop “Continual learning in biological and artificial neural networks” (more)
Title: “Continual learning through synaptic intelligence”
The talk will be largely based on:
- Zenke, F.*, Poole, B.*, and Ganguli, S. (2017).
Continual Learning Through Synaptic Intelligence.
Proceedings of the 34th International Conference on Machine Learning (ICML), pp. 3987–3995.
fulltext | preprint | code | talk
9:40-10:10 on Tuesday, 5th of March 2019 in the workshop “Why spikes? – Understanding the power and constraints of spiking based computation in biological and artificial neuronal networks” (more)
Title: “Computation in spiking neural networks — Opportunities and challenges”
I will talk about unpublished results and:
- Neftci, E.O., Mostafa, H., and Zenke, F. (2019).
Surrogate Gradient Learning in Spiking Neural Networks.
ArXiv:1901.09948 [Cs, q-Bio].
- Zenke, F. and Ganguli, S. (2018).
SuperSpike: Supervised learning in multi-layer spiking neural networks.
Neural Comput 30, 1514–1541. doi: 10.1162/neco_a_01086
fulltext | preprint | code