Competitive STDP-Based Spike Pattern Learning

By Timothée Masquelier, R. Guyonneau, Simon J. Thorpe. Neural Computation, 2009

330 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/80b01ba81ac71d574a761f85682d6d3f8014685e

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

STDP and STDP variations with memristors for spiking neuromorphic learning systems (2013, background): “…timing of individual pre-and/or post-synaptic spikes (Gerstner et al., 1993, 1996; Markram et al., 1997; Bi and Poo, 1998, 2001; Zhang et al., 1998; Feldman, 2000; Mu and Poo, 2006; Cassenaer and Laurent, 2007; Jacob et al., 2007; Young, 2007; Finelli et al., 2008; Masquelier et al., 2008, 2009).”
Learning in spiking neural networks (2013, methodology): “Winner-take-all (WTA) networks provide explicit functional coding - either for the formation of stimulus-selective cortical columns (Song and Abbott, 2001), or for methods of pattern selection (Masquelier et al., 2008a).”
Deep Learning With Spiking Neurons: Opportunities and Challenges (2018, background): “In competitive networks, STDP is capable of solving unsupervised learning tasks such as clustering (Masquelier et al., 2009; Nessler et al., 2013).”
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity (2013, background): “Competitive pattern learning through STDP has recently been studied in [68].”
On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex (2011, methodology): “…(Goodman and Brette, 2008) and a conventional additive STDP learning rule; (2) by using a dedicated event based simulator adapted from reference (Masquelier et al., 2009a) implementing the quadratic STDP learning rule of the memristors and the neuron dynamics corresponding to the circuit in…”
Emergence of Dynamic Memory Traces in Cortical Microcircuit Models through STDP (2013, background): “Recently, it has been shown that this mechanism also works very well for STDP in a WTA circuit (Gupta and Long, 2009; Masquelier et al., 2009).”
Learning through ferroelectric domain dynamics in solid-state synapses (2017, neutral): “This makes STDP the basis for autonomous, unsupervised learning 6 .”
Evolvable Neuronal Paths: A Novel Basis for Information and Search in the Brain (2011, background): “The forth modification to Izhikevich is that in addition to weight change due to DA modulated eligibility traces based on STDP, there is an activity dependent linear weight decay of 0.00002 mV per ms, if a neuron does not fire at all in one second; this results in a weight reaching the minimum permi”
The spike timing dependence of plasticity (2012, background): “STDP can also mediate temporal difference learning (Rao and Sejnowski, 2003) and reinforcement learning (Farries and Fairhall, 2007; Izhikevich, 2007; Cassenaer and Laurent, 2012) and can tune neurons for temporal features of input (Masquelier et al., 2009).”
Olfactory information processing in Drosophila. (2009, neutral): “A recent theoretical study has demonstrated that a very similar arrangement can learn in an unsupervised way to extract regular patterns from noisy spike trains [ 113 ].”
Neural syntax: cell assemblies, synapsembles and readers (2010, neutral): “Learning to discriminate numerous patterns requires increasing numbers of selective readers (Masquelier et al., 2009).”