Biologically Inspired Temporal Sequence Learning
نویسندگان
چکیده
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 interstimulus 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. © 2012 The Authors. Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Centre of Humanoid Robots and Bio-Sensor (HuRoBs), Faculty of Mechanical Engineering, Universiti Teknologi MARA.
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