Polychronization: Computation with Spikes
Neural Computation
Competitive stdp-based spike pattern learning
Neural Computation
Simple model of spiking neurons
IEEE Transactions on Neural Networks
Which model to use for cortical spiking neurons?
IEEE Transactions on Neural Networks
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Axonal conduction delays should not be ignored in simulations of spiking neural networks. Here it is shown that by using axonal conduction delays, neurons can display sensitivity to a specific spatiotemporal spike pattern. By using delays that complement the firing times in a pattern, spikes can arrive simultaneously at an output neuron, giving it a high chance of firing in response to that pattern. An unsupervised learning mechanism called spike-timing-dependent plasticity then increases the weights for connections used in the pattern, and decreases the others. This allows for an attunement of output neurons to specific activity patterns, based on temporal aspects of axonal conductivity.