Abstract
We propose an associative learning model using reward modulated spike-time dependent plasticity in reinforcement learning paradigm. The task of learning is to associate a stimulus pair, known as the predictor − choice pair, to a target response. In our model, a generic architecture of neural network has been used, with minimal assumption about the network dynamics. We demonstrate that stimulus-stimulus-response association can be implemented in a stochastic way within a noisy setting. The network has rich dynamics resulting from its recurrent connectivity and background activity. The algorithm can learn temporal sequence detection and solve temporal XOR problem.
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© 2012 Springer-Verlag Berlin Heidelberg
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Yusoff, N., Grüning, A., Notley, S. (2012). Pair-Associate Learning with Modulated Spike-Time Dependent Plasticity. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33269-2_18
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DOI: https://doi.org/10.1007/978-3-642-33269-2_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33268-5
Online ISBN: 978-3-642-33269-2
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