Varieties of Helmholtz machine
Neural Networks - 1996 Special issue: four major hypotheses in neuroscience
Antidromic Spikes Drive Hebbian Learning in an Artificial Dendritic Tree
Analog Integrated Circuits and Signal Processing - Special issue on Learning on Silicon
Temporally asymmetric Hebbian learning, spike timing and neuronal response variability
Proceedings of the 1998 conference on Advances in neural information processing systems II
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Reinforcement Learning in Continuous Time and Space
Neural Computation
Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience Series)
Hebbian imprinting and retrieval in oscillatory neural networks
Neural Computation
Does Morphology Influence Temporal Plasticity?
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Isotropic sequence order learning
Neural Computation
Self-organizing dual coding based on spike-time-dependent plasticity
Neural Computation
Reinforcement Learning, Spike-Time-Dependent Plasticity, and the BCM Rule
Neural Computation
Dynamic Brain - from Neural Spikes to Behaviors
A spiking neural network model of an actor-critic learning agent
Neural Computation
Adaptive synchronization of activities in a recurrent network
Neural Computation
Proceedings of the 2009 conference on Neural Nets WIRN09: Proceedings of the 19th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, May 28--30 2009
An unbiased implementation of regularization mechanisms
Image and Vision Computing
Spiking neuron model for temporal sequence recognition
Neural Computation
Implementing classical conditioning with spiking neurons
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Synaptic information transfer in computer models of neocortical columns
Journal of Computational Neuroscience
Phase precession and recession with STDP and Anti-STDP
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Spike-Timing dependent plasticity learning for visual-based obstacles avoidance
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Nanoscale electronic synapses using phase change devices
ACM Journal on Emerging Technologies in Computing Systems (JETC) - Special issue on memory technologies
Toward nonlinear local reinforcement learning rules through neuroevolution
Neural Computation
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A spike-timing-dependent Hebbian mechanism governs the plasticity of recurrent excitatory synapses in the neocortex: synapses that are activated a few milliseconds before a postsynaptic spike are potentiated, while those that are activated a few milliseconds after are depressed. We show that such a mechanism can implement a form of temporal difference learning for prediction of input sequences. Using a biophysical model of a cortical neuron, we show that a temporal difference rule used in conjunction with dendritic backpropagating action potentials reproduces the temporally asymmetric window of Hebbian plasticity observed physio-logically. Furthermore, the size and shape of the window vary with the distance of the synapse from the soma. Using a simple example, we show how a spike-timing-based temporal difference learning rule can allow a network of neocortical neurons to predict an input a few milliseconds before the input's expected arrival.