Spikes: exploring the neural code
Spikes: exploring the neural code
Short-term synaptic plasticity and network behavior
Neural Computation
A spiking neuron model: applications and learning
Neural Networks
Isotropic sequence order learning
Neural Computation
Self-organizing dual coding based on spike-time-dependent plasticity
Neural Computation
What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?
Neural Computation
Intrinsic Stabilization of Output Rates by Spike-Based Hebbian Learning
Neural Computation
Spike-Timing-Dependent Plasticity in Balanced Random Networks
Neural Computation
Optimal Hebbian learning: a probabilistic point of view
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Neural Processing Letters
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We present a spiking neuron model that allows for an analytic calculation of the correlations between pre- and postsynaptic spikes. The neuron model is a generalization of the integrate-and-fire model and equipped with a probabilistic spike-triggering mechanism. We show that under certain biologically plausible conditions, pre- and postsynaptic spike trains can be described simultaneously as an inhomogeneous Poisson process.Inspired by experimental findings, we develop a model for synaptic long-term plasticity that relies on the relative timing of pre- and postsynaptic action potentials. Being given an input statistics, we compute the stationary synaptic weights that result from the temporal correlations between the pre- and postsynaptic spikes. By means of both analytic calculations and computer simulations, we show that such a mechanism of synaptic plasticity is able to strengthen those input synapses that convey precisely timed spikes at the expense of synapses that deliver spikes with a broad temporal distribution. This may be of vital importance for any kind of information processing based on spiking neurons and temporal coding.