Neural processing in the subsecond time range in the temporal cortex
Neural Computation
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Self-Organization through Spike-Timing Dependent Plasticity Using localized Synfire-Chain Patterns
Neural Processing Letters
Spike-Timing-Dependent Plasticity in Balanced Random Networks
Neural Computation
Unsupervised Learning of Head Pose through Spike-Timing Dependent Plasticity
PIT '08 Proceedings of the 4th IEEE tutorial and research workshop on Perception and Interactive Technologies for Speech-Based Systems: Perception in Multimodal Dialogue Systems
Competitive stdp-based spike pattern learning
Neural Computation
First-spike latency in the presence of spontaneous activity
Neural Computation
Self-organization through spike-timing dependent plasticity using localized synfire-chain patterns
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Spike-Based image processing: can we reproduce biological vision in hardware?
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Spike-timing-dependent construction
Neural Computation
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Spike timing-dependent plasticity (STDP) is a learning rule that modifies the strength of a neuron's synapses as a function of the precise temporal relations between input and output spikes. In many brains areas, temporal aspects of spike trains have been found to be highly reproducible. How will STDP affect a neuron's behavior when it is repeatedly presented with the same input spike pattern? We show in this theoretical study that repeated inputs systematically lead to a shaping of the neuron's selectivity, emphasizing its very first input spikes, while steadily decreasing the postsynaptic response latency. This was obtained under various conditions of background noise, and even under conditions where spiking latencies and firing rates, or synchrony, provided conflicting informations. The key role of first spikes demonstrated here provides further support for models using a single wave of spikes to implement rapid neural processing.