Spiking neural network with lateral inhibition for reward-based associative learning
In this paper we propose a lateral inhibitory spiking neural network for reward-based associative learning with correlation in spike patterns for conflicting responses. The network has random and sparse connectivity, and we introduce a lateral inhibition via an anatomical constraint and synapse rein...
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Springer International Publishing
2014
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_version_ | 1803627595165597696 |
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author | Yusoff, Nooraini Ahmad, Farzana Kabir |
author2 | Chu, Kiong Loo |
author_facet | Chu, Kiong Loo Yusoff, Nooraini Ahmad, Farzana Kabir |
author_sort | Yusoff, Nooraini |
collection | UUM |
description | In this paper we propose a lateral inhibitory spiking neural network for reward-based associative learning with correlation in spike patterns for conflicting responses. The network has random and sparse connectivity, and we introduce a lateral inhibition via an anatomical constraint and synapse reinforcement. The spiking dynamic follows the properties of Izhikevich spiking model. The learning involves association of a delayed stimulus pair to a response using reward modulated spike-time dependent plasticity (STDP). The proposed learning scheme has improved our initial work by allowing learning in a more dynamic and competitive environment. |
first_indexed | 2024-07-04T06:08:33Z |
format | Book Section |
id | uum-18762 |
institution | Universiti Utara Malaysia |
last_indexed | 2024-07-04T06:08:33Z |
publishDate | 2014 |
publisher | Springer International Publishing |
record_format | dspace |
spelling | uum-187622016-10-04T08:08:53Z https://repo.uum.edu.my/id/eprint/18762/ Spiking neural network with lateral inhibition for reward-based associative learning Yusoff, Nooraini Ahmad, Farzana Kabir QA75 Electronic computers. Computer science In this paper we propose a lateral inhibitory spiking neural network for reward-based associative learning with correlation in spike patterns for conflicting responses. The network has random and sparse connectivity, and we introduce a lateral inhibition via an anatomical constraint and synapse reinforcement. The spiking dynamic follows the properties of Izhikevich spiking model. The learning involves association of a delayed stimulus pair to a response using reward modulated spike-time dependent plasticity (STDP). The proposed learning scheme has improved our initial work by allowing learning in a more dynamic and competitive environment. Springer International Publishing Chu, Kiong Loo Keem, Siah Yap Kok, Wai Wong Andrew, Teoh Kaizhu, Huang 2014 Book Section PeerReviewed Yusoff, Nooraini and Ahmad, Farzana Kabir (2014) Spiking neural network with lateral inhibition for reward-based associative learning. In: Neural Information Processing. Lecture Notes in Computer Science, 8834 . Springer International Publishing, Switzerland, pp. 327-334. ISBN 978-3-319-12636-4 http://doi.org/10.1007/978-3-319-12637-1_41 doi:10.1007/978-3-319-12637-1_41 doi:10.1007/978-3-319-12637-1_41 |
spellingShingle | QA75 Electronic computers. Computer science Yusoff, Nooraini Ahmad, Farzana Kabir Spiking neural network with lateral inhibition for reward-based associative learning |
title | Spiking neural network with lateral inhibition for reward-based associative learning |
title_full | Spiking neural network with lateral inhibition for reward-based associative learning |
title_fullStr | Spiking neural network with lateral inhibition for reward-based associative learning |
title_full_unstemmed | Spiking neural network with lateral inhibition for reward-based associative learning |
title_short | Spiking neural network with lateral inhibition for reward-based associative learning |
title_sort | spiking neural network with lateral inhibition for reward based associative learning |
topic | QA75 Electronic computers. Computer science |
work_keys_str_mv | AT yusoffnooraini spikingneuralnetworkwithlateralinhibitionforrewardbasedassociativelearning AT ahmadfarzanakabir spikingneuralnetworkwithlateralinhibitionforrewardbasedassociativelearning |