Spike-timing Dependent Competitive Learning of Integrate-and-Fire Neurons with Active Dendrites

Presented is a model of an integrate-and-fire neuron with active dendrites and a spike-timing dependent Hebbian learning rule. The learning algorithm effectively trains the neuron when responding to several types of temporal encoding schemes: temporal code with single spikes, spike bursts and phase coding. The neuron model and learning algorithm are tested on a neural network with a self-organizing map of competitive neurons. The goal of the presented work is to develop computationally efficient models rather than approximating the real neurons. The approach described in this paper demonstrates the potential advantages of using the processing functionalities of active dendrites as a novel paradigm of computing with networks of artificial spiking neurons.

@InProceedings{PWC02, 
 	 author =  {Panchev, Christo and Wermter, Stefan and Chen, Huixin},  
 	 title = {Spike-timing Dependent Competitive Learning of Integrate-and-Fire Neurons with Active Dendrites}, 
 	 booktitle = {Proceedings of the International Conference on Artificial Neural Networks},
 	 number = {},
 	 volume = {},
 	 pages = {896--901},
 	 year = {2002},
 	 month = {Aug},
 	 publisher = {Springer},
 	 doi = {10.1007/3-540-46084-5_145}, 
 }