Supervised Associative Learning in Spiking Neural Network
Yusoff, N and Grüning, A (2010) Supervised Associative Learning in Spiking Neural Network In: Artificial Neural Networks – ICANN 2010, 2010-09-15 - 2010-09-18, Thessaloniki, Greece.
icann_2010_preprint.pdf - Accepted version Manuscript
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.
|Item Type:||Conference or Workshop Item (UNSPECIFIED)|
|Divisions :||Faculty of Engineering and Physical Sciences > Computing Science|
|Identification Number :||https://doi.org/10.1007/978-3-642-15819-3_30|
|Related URLs :|
|Additional Information :||The original publication is available at http://www.springerlink.com|
|Depositing User :||Symplectic Elements|
|Date Deposited :||14 Dec 2011 11:24|
|Last Modified :||23 Sep 2013 18:53|
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