The last years have seen many exciting new developments to train spiking neural networks to perform complex information processing. This online workshop brings together researchers in the field to present their work and discuss ways of translating these findings into a better understanding of neural circuits. Topics include artificial and biologically plausible learning algorithms and the dissection of trained spiking circuits toward understanding neural processing. We have a manageable number of talks with ample time for discussions.

The workshop is being organised by Dan Goodman and Friedemann Zenke.

World Wide Neuro

Business Year

Spiking neural networks as universal function approximators

Julian Goeltz (Uni Bern) - Fast and deep neuromorphic learning with time-to-first-spike coding

Iulia M. Comsa (Google) - On temporal coding in spiking neural networks with alpha synaptic function

Welcome

Richard Naud (U Ottawa) - Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits

Timothee Masquelier (CNRS Toulouse) - Back-propagation in spiking neural networks

Emre Neftci (UC Irvine) - Synthesizing Machine Intelligence in Neuromorphic Computers with Differentiable Programming

Franz Scherr (TUG) - E-prop: A biologically inspired paradigm for learning in recurrent networks of spiking neurons

Welcome (day 2)

Sander Bohte (CWI) - Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks