Learning continuous attractors in recurrent networks
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Synaptic pruning in development: a computational account
Neural Computation
Fast calculation of short-term depressing synaptic conductances
Neural Computation
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
Spike-Timing Dependent Competitive Learning of Integrate-and-Fire Neurons with Active Dendrites
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Does Morphology Influence Temporal Plasticity?
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Isotropic sequence order learning
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
Stabilising hebbian learning with a third factor in a food retrieval task
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
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Spike-timing-dependent plasticity (STDP) is described by long-term potentiation (LTP), when a presynaptic event precedes a postsynaptic event, and by long-term depression (LTD), when the temporal order is reversed. In this article, we present a biophysical model of STDP based on a differential Hebbian learning rule (ISO learning). This rule correlates presynaptically the NMDA channel conductance with the derivative of the membrane potential at the synapse as the postsynaptic signal. The model is able to reproduce the generic STDP weight change characteristic. We find that (1) The actual shape of the weight change curve strongly depends on the NMDA channel characteristics and on the shape of the membrane potential at the synapse. (2) The typical antisymmetrical STDP curve (LTD and LTP) can become similar to a standard Hebbian characteristic (LTP only) without having to change the learning rule. This occurs if the membrane depolarization has a shallow onset and is long lasting. (3) It is known that the membrane potential varies along the dendrite as a result of the active or passive backpropagation of somatic spikes or because of local dendritic processes. As a consequence, our model predicts that learning properties will be different at different locations on the dendritic tree. In conclusion, such site-specific synaptic plasticity would provide a neuron with powerful learning capabilities.