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.
CITATION STYLE
Gardner, B., Sporea, I., & Grüning, A. (2014). Classifying spike patterns by reward-modulated STDP. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8681 LNCS, pp. 749–756). Springer Verlag. https://doi.org/10.1007/978-3-319-11179-7_94
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