Spike-timing dependent plasticity

By Jesper Sjöström, Wulfram Gerstner. Scholarpedia, 2010

299 citations recorded by the source corpus.

Spike Timing Dependent Plasticity (STDP) is a temporally asymmetric form of Hebbian learning induced by tight temporal correlations between the spikes of pre- and postsynaptic neurons. As with other forms of synaptic plasticity, it is widely believed that it underlies learning and information storage in the brain, as well as the development and refinement of neuronal circuits during brain development (e.g. Bi and Poo, 2001; Sjostrom et al., 2008). With STDP, repeated presynaptic spike arrival a few milliseconds before postsynaptic action potentials leads in many synapse types to long-term potentiation (LTP) of the synapses, whereas repeated spike arrival after postsynaptic spikes leads to long-term depression (LTD) of the same synapse. The change of the synapse plotted as a function of the relative timing of pre- and postsynaptic action potentials is called the STDP function or learning window and varies between synapse types. The rapid change of the STDP function with the relative timing of spikes suggests the possibility of temporal coding schemes on a millisecond time scale.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/d3302f754b44807b2fd8b952a1c09d00216ef279

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Learning in spiking neural networks (2013, background): “These rules try to explain a biological effect which shows synaptic depression when neurons are subject to high input firing rates, where standard STDP would potentiate those synapses (Sjöström and Gerstner, 2010).”
Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules (2018, result): “, 2006) which is in turn closely related to other nonlinear STDP models (Senn et al., 2001; Froemke and Dan, 2002; Izhikevich and Desai, 2003) as well as to the BCM model discussed above (Pfister et al.”
Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules (2016, background): “, 2001, can be considered as a temporally precise form of Hebbian synaptic plasticity, induced by isolated spikes in pre- and postsynaptic neurons (for reviews see e.g., Abbott and Nelson, 2000; Bi and Poo, 2001; Caporale and Dan, 2008; Sjöström et al., 2008; Sjöström and Gerstner, 2010; Markram et ”