A self-organizing neural network model of the primary visual cortex
A self-organizing neural network model of the primary visual cortex
Spike-Timing Dependent Competitive Learning of Integrate-and-Fire Neurons with Active Dendrites
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
A new view of the primary visual cortex
Neural Networks - 2004 Special issue Vision and brain
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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Increasing amount of evidence suggests that the brain has the necessary mechanisms to and indeed does generate and process temporal information from the very early stages of sensory pathways. This paper presents a novel biologically motivated model of the visual system based on temporal encoding of the visual stimuli and temporally precise lateral geniculate nucleus (LGN) spikes. The work investigates whether such a network could be developed using an extended type of integrate-and-fire neurons (ADDS) and trained to recognise objects of different shapes using STDP learning. The experimental results contribute further support to the argument that temporal encoding can provide a mechanism for representing information in the visual system and has the potential to complement firing-rate-based architectures toward building more realistic and powerful models.