This paper presents a hybrid learning algorithmfor spiking neural networks (SNNs), referred to as an evolvablespiking neural network (ESNN) paradigm. The algorithmintegrates a supervised and unsupervised learning approach.The unsupervised approach exploits a Spike Timing DependentPlasticity (STDP) mechanism with explicit delay learningfor multiple connections between neurons. Supervision of thesynaptic delays and the excitatory/inhibitory connections isgoverned by a genetic algorithm (GA), while the STDP rule isfree to operate in its normal unsupervised manner. A spike trainencoding/decoding scheme is developed for the algorithm. Theapproach is validated by application to the Iris classificationproblem.
|Title of host publication||Unknown Host Publication|
|Number of pages||6|
|Publication status||Published - Sep 2006|
|Event||3rd IEEE International conference on Intelligent Systems, Westminster, London, September 06 - |
Duration: 1 Sep 2006 → …
|Conference||3rd IEEE International conference on Intelligent Systems, Westminster, London, September 06|
|Period||1/09/06 → …|