Spike-Timing Dependent Competitive Learning of Integrate-and-Fire Neurons with Active Dendrites
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Online temporal pattern learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
An oscillatory model for multimodal processing of short language instructions
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
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This paper introduces a new model of a spiking neuron with active dendrites and dynamic synapses (ADDS). The neuron employs the dynamics of the synapses and the active properties of the dendrites as an adaptive mechanism for maximising its response to a specific spatio-temporal distribution of incoming action potentials. The paper also presents a new spike-timing-dependent plasticity (STDP) algorithm developed for the ADDS neuron. This algorithm follows recent biological evidence on synaptic plasticity, and goes beyond the current computational approaches which are based only on the relative timing between single pre- and post-synaptic spikes and implements a functional dependence based on the state of the dendritic and somatic membrane potentials at the time of the post-synaptic spike.