Spike-Timing-Dependent Plasticity: A Comprehensive Overview

By Henry Markram, Wulfram Gerstner, P. J. Sjöström. Front. Syn. Neurosci., 2012

366 citations recorded by the source corpus.

Reference EPFL-ARTICLE-183375doi:10.3389/fnsyn.2012.00002View record in PubMed Record created on 2013-01-28, modified on 2017-05-12

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

View Paper: https://www.semanticscholar.org/paper/b97a40067721a2b84f47377fee7b626de108c2bc

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

The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics (2014, background): “For example, there might be two sets of learning rules operating in parallel, one set of rules which learns that statistical structure of the input, perhaps mainly through the plasticity of excitatory-to-excitatory connections [68], and another which tunes network dynamics, perhaps primarily by inhi”
Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation (2016, background): “Spike-Timing Dependent Plasticity (STDP) is believed to be a prominent form of synaptic change in neurons (Markram and Sakmann, 1995; Gerstner et al. , 1996), and see Markram et al. (2012) for a review.”
An objective function for STDP (2015, background): “See Markram et al. (2012) for a recent review of the neuroscience research on STDP.”
A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses (2015, background): “Many models of Spike-Timing Dependent Plasticity (STDP) have been proposed in the computational neuroscience literature (Abbott and Nelson, 2000; Markram et al., 2012).”
A 0.086-mm$^2$ 12.7-pJ/SOP 64k-Synapse 256-Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28-nm CMOS (2018, background): “The time-multiplexed SDSP learning rule is compatible with standard foundry SRAMs in order to store the synaptic states, as opposed to the digital time-multiplexed probabilistic STDP synapse proposed by Seo et al. in [28], which requires a custom dual-port SRAM with row and column accesses.”
Memory and Information Processing in Neuromorphic Systems (2015, background): “A popular class of LTP mechanisms that has been the subject of widespread interest within the neuroscience community [90], the neuromorphic community [91], and more recently in the material science and nanotechnology community [20], [92], is based on the spike-timing-dependent plasticity (STDP) rule”
Learning in spiking neural networks (2013, background): “The best-known spiking neural network learning paradigm is Spike Timing Dependent Plasticity (STDP) (Markram et al., 2012) which adjusts the strength of a connection in response to the time difference between the pre- and post-synaptic spikes.”