Pair-associate learning with modulated spike-time dependent plasticity
We propose an associative learning model using reward modulated spike-time dependent plasticity in reinforcement learning paradigm. The task of learning is to associate a stimulus pair, known as the predictor−choice pair, to a target response.In our model, a generic architecture of neural network ha...
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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/12489/ http://dx.doi.org/10.1007/978-3-642-33269-2_18 |
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| Summary: | We propose an associative learning model using reward modulated spike-time dependent plasticity in reinforcement learning paradigm. The task of learning is to associate a stimulus pair, known as the predictor−choice pair, to a target response.In our model, a generic architecture of neural network has been used, with minimal assumption about the network dynamics.We demonstrate that stimulus-stimulus-response association can be implemented in a stochastic way within a noisy setting.The network has rich dynamics resulting from its recurrent connectivity and background activity. The algorithm can learn temporal sequence detection and solve temporal XOR problem. |
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