Exploring unsupervised online learning in digital spiking neural networks

In this workgroup, we will explore unsupervised online learning with the ODIN 28-nm digital spiking neural network, if possible applied to real-world examples (e.g., the MNIST dataset).
Compared to analog approaches, digital implementations of online learning rules:
- do not suffer from variations in operating voltage or temperature,
- allow one-to-one hardware/software correspondence,
- keep the stochasticity and random-selection properties for Poisson-distributed input spike trains.

ODIN is an online-learning digital spiking neural network in 28nm CMOS recently developed at Université catholique de Louvain.
As only one chip testbench is available for in silico experimentation, a custom Python simulator of ODIN will be shared for quick exploration of parameters and network topologies.

The wiki page contains a preprint of ODIN together with useful papers and links.

Go to group wiki


Day Time Location
Wed, 25.04.2018 15:00 - 16:00 Sala Panorama (Hotel 1st floor, above main hall)
Thu, 26.04.2018 22:00 - 23:00 Sala Panorama
Fri, 27.04.2018 14:00 - 15:00 Lecture Room
Mon, 30.04.2018 15:00 - 16:00 Sala Panorama


Charlotte Frenkel


Sebastian Billaudelle
Benjamin Cramer
Jakub Fil
Ismael Tito Freire González
Christos Giotis
Hector Gonzalez
Álvaro González
jacques kaiser
aamir khan
Raphaela Kreiser
Dongchen Liang
Daniel Mannion
Emre Neftci
Carsten Nielsen
Omar Oubari
Johannes Partzsch
Ning Qiao
Nicoletta Risi
Fredrik Sandin
Baris Serhan
Alan Stokes
Yannik Stradmann
Michiel Van Dyck
Annika Weisse
Jayawan Wijekoon
Xi Wu
Bojian Yin
Ali Zeinolabedin