Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
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 Hebbian Plasticity as Temporal Difference Learning
Neural Computation
Simple model of spiking neurons
IEEE Transactions on Neural Networks
Delay learning and polychronization for reservoir computing
Neurocomputing
A gradient learning rule for the tempotron
Neural Computation
Learning spike-based population codes by reward and population feedback
Neural Computation
Optical Memory and Neural Networks
Motoneuron membrane potentials follow a time inhomogeneous jump diffusion process
Journal of Computational Neuroscience
Does high firing irregularity enhance learning?
Neural Computation
Advances in Artificial Neural Systems - Special issue on Advances in Unsupervised Learning Techniques Applied to Biosciences and Medicine
A new supervised learning algorithm for spiking neurons
Neural Computation
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In timing-based neural codes, neurons have to emit action potentials at precise moments in time. We use a supervised learning paradigm to derive a synaptic update rule that optimizes by gradient ascent the likelihood of postsynaptic firing at one or several desired firing times. We find that the optimal strategy of up- and downregulating synaptic efficacies depends on the relative timing between presynaptic spike arrival and desired postsynaptic firing. If the presynaptic spike arrives before the desired postsynaptic spike timing, our optimal learning rule predicts that the synapse should become potentiated. The dependence of the potentiation on spike timing directly reflects the time course of an excitatory postsynaptic potential. However, our approach gives no unique reason for synaptic depression under reversed spike timing. In fact, the presence and amplitude of depression of synaptic efficacies for reversed spike timing depend on how constraints are implemented in the optimization problem. Two different constraints, control of postsynaptic rates and control of temporal locality, are studied. The relation of our results to spike-timing-dependent plasticity and reinforcement learning is discussed.