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A neuromorphic network for generic multivariate data classification
journal contribution
posted on 2023-06-08, 18:22 authored by Michael SchmukerMichael Schmuker, Thomas Pfeil, Martin Paul NawrotComputational neuroscience has uncovered a number of computational principles used by nervous systems. At the same time, neuromorphic hardware has matured to a state where fast silicon implementations of complex neural networks have become feasible. En route to future technical applications of neuromorphic computing the current challenge lies in the identification and implementation of functional brain algorithms. Taking inspiration from the olfactory system of insects, we constructed a spiking neural network for the classification of multivariate data, a common problem in signal and data analysis. In this model, real-valued multivariate data are converted into spike trains using "virtual receptors" (VRs). Their output is processed by lateral inhibition and drives a winner-take-all circuit that supports supervised learning. VRs are conveniently implemented in software, whereas the lateral inhibition and classification stages run on accelerated neuromorphic hardware. When trained and tested on real-world datasets, we find that the classification performance is on par with a naïve Bayes classifier. An analysis of the network dynamics shows that stable decisions in output neuron populations are reached within less than 100 ms of biological time, matching the time-to-decision reported for the insect nervous system. Through leveraging a population code, the network tolerates the variability of neuronal transfer functions and trial-to-trial variation that is inevitably present on the hardware system. Our work provides a proof of principle for the successful implementation of a functional spiking neural network on a configurable neuromorphic hardware system that can readily be applied to real-world computing problems.
History
Publication status
- Published
Journal
Proceedings of the National Academy of Sciences of the United States of AmericaISSN
0027-8424Publisher
National Academy of SciencesExternal DOI
Issue
6Volume
111Page range
2081-2086Department affiliated with
- Informatics Publications
Full text available
- No
Peer reviewed?
- Yes