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Biologically inspired temporal sequence learning


Yusoff, Nooraini and Grüning, André (2012) Biologically inspired temporal sequence learning. Procedia Engineering, 41. pp. 319-325. ISSN 1877-7058

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Abstract

We propose a temporal sequence learning model in spiking neural networks consisting of Izhikevich spiking neurons.In our reward-based learning model, we train a network to associate two stimuli with temporal delay and a target response. Learning rule is dependent on reward signals that modulate the weight changes derived from spike-timing dependent plasticity (STDP) function.The dynamic properties of our model can be attributed to the sparse and recurrent connectivity, synaptic transmission delays, background activity and inter-stimulus interval (ISI).We have tested the learning in visual recognition task, and temporal AND and XOR problems.The network can be trained to associate a stimulus pair with its target response and to discriminate the temporal sequence of the stimulus presentation.

Item Type: Article
Additional Information: International Symposium on Robotics and Intelligent Sensors 2012 (IRIS 2012)
Uncontrolled Keywords: Temporal sequence learning;Spiking neural networks; Spike-timing dependent plasticity; Reward-based learning
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: College of Arts and Sciences
Depositing User: Dr. Nooraini Yusoff
Date Deposited: 26 Oct 2014 03:10
Last Modified: 26 Oct 2014 03:10
URI: http://repo.uum.edu.my/id/eprint/12490

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