Stimulus-stimulus association via reinforcement learning in spiking neural network
In this paper, we propose an algorithm that performs stimulus-stimulus association via reinforcement learning.In particular, we develop a recurrent network with dynamic properties of Izhikevich spiking neuron model and train the network to associate a stimulus pair using reward modulated spike-time...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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2013
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| Subjects: | |
| Online Access: | https://repo.uum.edu.my/id/eprint/12504/1/069.pdf |
| _version_ | 1825803018651566080 |
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| author | Yusoff, Nooraini Kabir Ahmad, Farzana |
| author_facet | Yusoff, Nooraini Kabir Ahmad, Farzana |
| author_sort | Yusoff, Nooraini |
| collection | UUM |
| description | In this paper, we propose an algorithm that performs stimulus-stimulus association via reinforcement learning.In particular, we develop a recurrent network with dynamic properties of Izhikevich spiking neuron model and train the network to associate a stimulus pair using reward modulated spike-time dependent plasticity.The learning algorithm associates a prime stimulus, known as the predictor, with a second stimulus, known as the choice, comes after an inter-stimulus interval.The influence of the prime stimulus on the neural response after the onset of the later stimulus is then observed.A series of probe trials resemble the retrospective and prospective activities in human response processing |
| first_indexed | 2024-07-04T05:50:04Z |
| format | Conference or Workshop Item |
| id | uum-12504 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T05:50:04Z |
| publishDate | 2013 |
| record_format | eprints |
| spelling | uum-125042014-10-26T06:50:22Z https://repo.uum.edu.my/id/eprint/12504/ Stimulus-stimulus association via reinforcement learning in spiking neural network Yusoff, Nooraini Kabir Ahmad, Farzana QA76 Computer software In this paper, we propose an algorithm that performs stimulus-stimulus association via reinforcement learning.In particular, we develop a recurrent network with dynamic properties of Izhikevich spiking neuron model and train the network to associate a stimulus pair using reward modulated spike-time dependent plasticity.The learning algorithm associates a prime stimulus, known as the predictor, with a second stimulus, known as the choice, comes after an inter-stimulus interval.The influence of the prime stimulus on the neural response after the onset of the later stimulus is then observed.A series of probe trials resemble the retrospective and prospective activities in human response processing 2013 Conference or Workshop Item PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/12504/1/069.pdf Yusoff, Nooraini and Kabir Ahmad, Farzana (2013) Stimulus-stimulus association via reinforcement learning in spiking neural network. In: 2013 13th International Conference on Intelligent Systems Design and Applications (ISDA), 8-10 Dec. 2013, Selangor, Malaysia. http://dx.doi.org/10.1109/ISDA.2013.6920722 doi:10.1109/ISDA.2013.6920722 doi:10.1109/ISDA.2013.6920722 |
| spellingShingle | QA76 Computer software Yusoff, Nooraini Kabir Ahmad, Farzana Stimulus-stimulus association via reinforcement learning in spiking neural network |
| title | Stimulus-stimulus association via reinforcement learning in spiking neural network |
| title_full | Stimulus-stimulus association via reinforcement learning in spiking neural network |
| title_fullStr | Stimulus-stimulus association via reinforcement learning in spiking neural network |
| title_full_unstemmed | Stimulus-stimulus association via reinforcement learning in spiking neural network |
| title_short | Stimulus-stimulus association via reinforcement learning in spiking neural network |
| title_sort | stimulus stimulus association via reinforcement learning in spiking neural network |
| topic | QA76 Computer software |
| url | https://repo.uum.edu.my/id/eprint/12504/1/069.pdf |
| work_keys_str_mv | AT yusoffnooraini stimulusstimulusassociationviareinforcementlearninginspikingneuralnetwork AT kabirahmadfarzana stimulusstimulusassociationviareinforcementlearninginspikingneuralnetwork |