University of Surrey

Test tubes in the lab Research in the ATI Dance Research

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.

[img] PDF
yusoff_iris_2012.pdf
Restricted to Repository staff only
Available under License : See the attached licence file.

Download (557kB)
[img] PDF (licence)
SRI_deposit_agreement.pdf
Restricted to Repository staff only

Download (33kB)

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: Conference or Workshop Item (UNSPECIFIED)
Authors :
AuthorsEmailORCID
Yusoff, NUNSPECIFIEDUNSPECIFIED
Grüning, AUNSPECIFIEDUNSPECIFIED
Date : 2012
Contributors :
ContributionNameEmailORCID
PublisherElsevier, UNSPECIFIEDUNSPECIFIED
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 14:08
Last Modified : 28 Mar 2017 14:08
URI: http://epubs.surrey.ac.uk/id/eprint/713375

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800