Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/80901
Title: An Online Unsupervised Structural Plasticity Algorithm for Spiking Neural Networks
Authors: Roy, Subhrajit
Basu, Arindam
Keywords: winner-take-all
spike-timing-dependent plasticity
Issue Date: 2016
Source: Roy, S., & Basu, A. (2016). An Online Unsupervised Structural Plasticity Algorithm for Spiking Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, in press.
Series/Report no.: IEEE Transactions on Neural Networks and Learning Systems
Abstract: In this paper, we propose a novel winner-take-all (WTA) architecture employing neurons with nonlinear dendrites and an online unsupervised structural plasticity rule for training it. Furthermore, to aid hardware implementations, our network employs only binary synapses. The proposed learning rule is inspired by spike-timing-dependent plasticity but differs for each dendrite based on its activation level. It trains the WTA network through formation and elimination of connections between inputs and synapses. To demonstrate the performance of the proposed network and learning rule, we employ it to solve two-class, four-class, and six-class classification of random Poisson spike time inputs. The results indicate that by proper tuning of the inhibitory time constant of the WTA, a tradeoff between specificity and sensitivity of the network can be achieved. We use the inhibitory time constant to set the number of subpatterns per pattern we want to detect. We show that while the percentages of successful trials are 92%, 88%, and 82% for two-class, four-class, and six-class classification when no pattern subdivisions are made, it increases to 100% when each pattern is subdivided into 5 or 10 subpatterns. However, the former scenario of no pattern subdivision is more jitter resilient than the later ones.
URI: https://hdl.handle.net/10356/80901
http://hdl.handle.net/10220/41059
ISSN: 2162-237X
DOI: 10.1109/TNNLS.2016.2582517
Schools: School of Electrical and Electronic Engineering 
Rights: © 2016 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. The published version is available at: [http://dx.doi.org/10.1109/TNNLS.2016.2582517].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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