Supervised associative learning in spiking neural network
In this paper, we propose a simple supervised associative learning approach for spiking neural networks. In an excitatory-inhibitory network paradigm with Izhikevich spiking neurons, synaptic plasticity is implemented on excitatory to excitatory synapses dependent on both spike emission rates and sp...
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| 主要な著者: | , |
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| フォーマット: | Book Section |
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Springer
2010
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| オンライン･アクセス: | http://repo.uum.edu.my/12487/ http://dx.doi.org/10.1007/978-3-642-15819-3_30 |
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| 要約: | In this paper, we propose a simple supervised associative learning approach for spiking neural networks. In an excitatory-inhibitory network paradigm with Izhikevich spiking neurons, synaptic plasticity is implemented on excitatory to excitatory synapses dependent on both spike emission rates and spike timings. As results of learning, the network is able to associate not just familiar stimuli but also novel stimuli observed through synchronised activity within the same subpopulation and between two associated subpopulations. |
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