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Classifying spike patterns by reward-modulated STDP

Gardner, B, Sporea, I and Grüning, A (2014) Classifying spike patterns by reward-modulated STDP Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8681 L. pp. 749-756.

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Reward-modulated learning rules for spiking neural networks have emerged, that have been demonstrated to solve a wide range of reinforcement learning tasks. Despite this, little work has aimed to classify spike patterns by the timing of output spikes. Here, we apply a rewardmaximising learning rule to teach a spiking neural network to classify input patterns by the latency of output spikes. Furthermore, we compare the performance of two escape rate functions that drive output spiking activity: the Arrhenius & Current (A&C) model and Exponential (EXP) model. We find A&C consistently outperforms EXP, and especially in terms of the time taken to converge in learning. We also show that jittering input patterns with a low noise amplitude leads to an improvement in learning, by reducing the variation in the performance. © 2014 Springer International Publishing Switzerland.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Gardner, B
Sporea, I
Grüning, A
Date : 2014
DOI : 10.1007/978-3-319-11179-7-94
Additional Information : The original publication is available at
Depositing User : Symplectic Elements
Date Deposited : 04 Nov 2014 17:11
Last Modified : 31 Oct 2017 17:05

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