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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| Main Authors: | , |
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| Format: | Book Section |
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Springer International Publishing
2014
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| Subjects: | |
| Online Access: | http://repo.uum.edu.my/18762/ http://doi.org/10.1007/978-3-319-12637-1_41 |
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| Summary: | 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. |
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