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Biologically Inspired Sequence Learning

Yusoff, N and Grüning, A (2012) Biologically Inspired Sequence Learning In: 2nd International Symposium on Robotics and Intelligent Sensors 2012, 2012-09-04 - 2012-09-06, Kuching, Sarawak MALAYSIA.

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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: Conference or Workshop Item (UNSPECIFIED)
Divisions : Surrey research (other units)
Authors : Yusoff, N and Grüning, A
Date : 2012
Contributors :
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 14:08
Last Modified : 23 Jan 2020 12:49

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