Computing with Spikes
The first NIPS Workshop Computing with Spikes will be held at NIPS 2016 in Barcelona, Spain, Saturday December 10. More details about the program are coming soon.
Despite remarkable computational success, artificial neural networks ignore the spiking nature of neural communication that is fundamental for biological neuronal networks. Understanding how spiking neurons process information and learn remains an essential challenge. It concerns not only neuroscientists studying brain function, but also neuromorphic engineers developing low-power computing architectures, or machine learning researchers devising new biologically-inspired learning algorithms. Unfortunately, despite a joint interest in spike-based computation, the interactions between these subfields remains limited. The workshop aims to bring them together and to foster the exchange between them by focusing on recent developments in efficient neural coding and spiking neurons' computation. The discussion will center around critical questions in the field, such as "what are the underlying paradigms?" "what are the fundamental constraints?", and "what are the measures for progress?”, that benefit from varied perspectives. The workshop will combine invited talks reviewing the state-of-the-art and short contributed presentations, and it will conclude with a panel discussion.
- Sophie Deneve (ENS, FRA)
- Wolfgang Maass (U. Graz, AUT)
- Steve Furber (U. Manchester, GBR)
- Tobi Delbrueck (ETH Zurich, SWI)
- Terry Steward (U Waterloo, CAN)
- Paul Merolla (IBM Truenorth team, USA)
Schedule for Computing with Spikes
Storage capacity of spatio-temporal patterns in LIF spiking networks: mixed rate and phase coding
Antonio de Candia and Siliva Scarpetta,
Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks
Bodo Rueckauer, Iulia-Alexandra Lungu, Yuhuang Hu, and Michael Pfeiffer
Somatic inhibition controls dendritic selectivity in a 2 sparse coding network of spiking neurons.
Fast and Efficient Asynchronous Neural Computation in Deep Adaptive Spiking Neural Networks
Davide Zambrano and Sander Bohte
Spiking memristor logic gates are a type of time-variant perceptron.
A wake-sleep algorithm for recurrent, spiking neural networks
Johannes Thiele, Peter Diehl and Matthew Cook
Deep counter networks for asynchronous event-based processing
Jonathan Binas, Giacomo Indiveri and Michael Pfeiffer
Spike-based reinforcement learning for temporal stimulus segmentation and decision making
Luisa Le Donne, Luca Mazzucato, Robert Urbanczik, Walter Senn and Giancarlo La Camera
Deep Spiking Networks
Peter O’Connor and Max Welling
Working Memory in Adaptive Spiking Neural Networks
Roeland Nusselder, Davide Zambrano and Sander Bohte
An Efficient Approach to Boosting Performance of Deep Spiking Network Training
Seongsik Park, Sung-gil Lee, Huynha Nam and Sungroh Yoon.
Optimization-based computation with spiking neurons
Stephen Verzi, Craig Vineyard, Eric Vugrin, Meghan Galiardi, Conrad James and James Aimone
Learning binary or real-valued time-series via spike-timing dependent plasticity
Towards deep learning with spiking neurons in energy based models with contrastive Hebbian plasticity
Thomas Mesnard, Wulfram Gerstner and Johanni Brea
Can we be formal in assessing the strengths and weaknesses of neural architectures? A case study using a spiking cross-correlation algorithm
William Severa, Kristofor Carlson, Ojas Parekh, Craig Vineyard and James Aimone
Nonnegative autoencoder with simplified random neural network
Yonghua Yin and Erol Gelenbe