Header

UZH-Logo

Maintenance Infos

Real-time inference in a VLSI spiking neural network


Corneil, D; Sonnleithner, D; Neftci, E; Chicca, E; Cook, M; Indiveri, G; Douglas, R (2012). Real-time inference in a VLSI spiking neural network. In: IEEE International Symposium on Circuits and Systems (ISCAS) 2012, Seoul, South Korea, 20 May 2012 - 23 May 2012, 2425-2428.

Abstract

The ongoing motor output of the brain depends on its remarkable ability to rapidly transform and fuse a variety of sensory streams in real-time. The brain processes these data using networks of neurons that communicate by asynchronous spikes, a technology that is dramatically different from conventional electronic systems. We report here a step towards constructing electronic systems with analogous performance to the brain. Our VLSI spiking neural network combines in real-time three distinct sources of input data; each is place-encoded on an individual neuronal population that expresses soft Winner-Take-All dynamics. These arrays are combined according to a user-specified function that is embedded in the reciprocal connections between the soft Winner-Take-All populations and an intermediate shared population. The overall network is able to perform function approximation (missing data can be inferred from the available streams) and cue integration (when all input streams are present they enhance one another synergistically). The network performs these tasks with about 80% and 90% reliability, respectively. Our results suggest that with further technical improvement, it may be possible to implement more complex probabilistic models such as Bayesian networks in neuromorphic electronic systems.

Abstract

The ongoing motor output of the brain depends on its remarkable ability to rapidly transform and fuse a variety of sensory streams in real-time. The brain processes these data using networks of neurons that communicate by asynchronous spikes, a technology that is dramatically different from conventional electronic systems. We report here a step towards constructing electronic systems with analogous performance to the brain. Our VLSI spiking neural network combines in real-time three distinct sources of input data; each is place-encoded on an individual neuronal population that expresses soft Winner-Take-All dynamics. These arrays are combined according to a user-specified function that is embedded in the reciprocal connections between the soft Winner-Take-All populations and an intermediate shared population. The overall network is able to perform function approximation (missing data can be inferred from the available streams) and cue integration (when all input streams are present they enhance one another synergistically). The network performs these tasks with about 80% and 90% reliability, respectively. Our results suggest that with further technical improvement, it may be possible to implement more complex probabilistic models such as Bayesian networks in neuromorphic electronic systems.

Statistics

Citations

Dimensions.ai Metrics
2 citations in Web of Science®
2 citations in Scopus®
7 citations in Microsoft Academic
Google Scholar™

Altmetrics

Downloads

26 downloads since deposited on 27 Feb 2013
9 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Conference or Workshop Item (Speech), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:23 May 2012
Deposited On:27 Feb 2013 16:24
Last Modified:29 Jul 2018 07:19
Series Name:IEEE International Symposium on Circuits and Systems. Proceedings
Number of Pages:4
ISSN:0271-4302
Additional Information:© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
OA Status:Green
Publisher DOI:https://doi.org/10.1109/ISCAS.2012.6271788
Related URLs:http://www.iscas2012.org/

Download

Download PDF  'Real-time inference in a VLSI spiking neural network'.
Preview
Content: Accepted Version
Filetype: PDF
Size: 272kB
View at publisher