Biologically inspired temporal sequence learning
We propose a temporal sequence learning model in spiking neural networks consisting of Izhikevich spiking neurons.In our reward-based learning model, we train a network to associate two stimuli with temporal delay and a target response. Learning rule is dependent on reward signals that modulate the...
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| 主要な著者: | , |
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| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
Elsevier Ltd.
2012
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| 主題: | |
| オンライン･アクセス: | http://repo.uum.edu.my/12490/1/1-s2.pdf http://repo.uum.edu.my/12490/ http://dx.doi.org/10.1016/j.proeng.2012.07.179 |
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| 要約: | We propose a temporal sequence learning model in spiking neural networks consisting of Izhikevich spiking neurons.In our reward-based learning model, we train a network to associate two stimuli with temporal delay and a target response. Learning rule is dependent on reward signals that modulate the weight changes derived from spike-timing dependent plasticity (STDP) function.The dynamic properties of our model can be attributed to the sparse and recurrent connectivity, synaptic transmission delays, background activity and inter-stimulus interval (ISI).We have tested the learning in visual recognition task, and temporal AND and XOR problems.The network can be trained to associate a stimulus pair with its target response and to discriminate the temporal sequence of the stimulus presentation. |
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