Learning anticipation through priming in spatio-temporal neural networks
In this paper, we propose a reward-based learning model inspired by the findings from a behavioural study and biologically realistic properties of spatio-temporal neural networks.The model simulates the cognitive priming effect in stimulus-stimulus-response association.Synaptic plasticity is depende...
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| Main Authors: | , |
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| Other Authors: | |
| Format: | Book Section |
| Published: |
Springer
2012
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| Subjects: | |
| Online Access: | http://repo.uum.edu.my/12488/ http://dx.doi.org/10.1007/978-3-642-34475-6_21 |
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| Institution: | Universiti Utara Malaysia |
| Summary: | In this paper, we propose a reward-based learning model inspired by the findings from a behavioural study and biologically realistic properties of spatio-temporal neural networks.The model simulates the cognitive priming effect in stimulus-stimulus-response association.Synaptic plasticity is dependent on a global reward signal that enhances the synaptic changes derived from spike-timing dependent plasticity (STDP) process.We show that by priming a network with a cue stimulus can facilitate the response to a later stimulus.The network can be trained to associate a stimulus pair (with an inter-stimulus interval) to a response, as well as to recognise the temporal sequence of the stimulus presentation. |
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