Equilibrium properties of temporally asymmetric Hebbian plasticity.

By Jonathan E. Rubin, Daniel D. Lee, Haim Sompolinsky. 2000

330 citations recorded by the source corpus.

A theory of temporally asymmetric Hebb rules, which depress or potentiate synapses depending upon whether the postsynaptic cell fires before or after the presynaptic one, is presented. Using the Fokker-Planck formalism, we show that the equilibrium synaptic distribution induced by such rules is highly sensitive to the manner in which bounds on the allowed range of synaptic values are imposed. In a biologically plausible multiplicative model, the synapses in asynchronous networks reach a distribution that is invariant to the firing rates of either the presynaptic or postsynaptic cells. When these cells are temporally correlated, the synaptic strength varies smoothly with the degree and phase of their synchrony.

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

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Citations in the corpus

Learning Only When Necessary: Better Memories of Correlated Patterns in Networks with Bounded Synapses (2005, background): “An interesting property of (multiplicative) synaptic saturation is that it tends to stabilize the synaptic weights (van Rossum, Bi, & Turrigiano, 2000; Rubin, Lee, & Sompolinsky, 2001).”
Optimal information storage and the distribution of synaptic weights: perceptron versus Purkinje cell. (2004, background): “Rossum et al., 2000; Song et al., 2000; Rubin et al., 2001; Cateau and Fukai, 2003; Gütig et al., 2003).”
Evolving spike-timing-dependent plasticity for single-trial learning in robots (2003, background): “The equilibrium weight distribution in this case tends to be unimodal and centred around the point where potentiation and depression equilibrate (Rubin et al. 2001).”
Heterosynaptic Plasticity Prevents Runaway Synaptic Dynamics (2013, methodology): “STDP and runaway synaptic dynamics STDP is experimentally well-characterized form of plasticity that is broadly used in computational models of learning and developmental processes (Miller, 1996; Kempter et al., 1999, 2001; Song et al., 2000; van Rossum et al., 2000; Rubin et al., 2001; Song and Abb”
On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex (2011, background): “On the other hand, in “multiplicative STDP” or mSTDP (van Rossum et al., 2000; Rubin et al., 2001; Gütig et al., 2003) the learning function is also a function of the actual weight value ξ m ( w , ∆ T ).”
Phenomenological models of synaptic plasticity based on spike timing (2008, methodology): “…average rate of change of synaptic strength corresponds to the drift of the random walk, which can be expressed in terms of the correlation between the pre- and postsynaptic spike trains (Kempter et al. 1999; Kistler and van Hemmen 2000; Kempter et al. 2001; Rubin et al. 2001; Gütig et al. 2003).”
Synaptic plasticity: taming the beast (2000, neutral): “STDP provides a mechanism for learning temporal sequences, but in some cases may require the addition of a synaptic scaling mechanism to generate sufficient competition between synapse”
STDP and STDP variations with memristors for spiking neuromorphic learning systems (2013, background): “Additive STDP requires the weight values to be bounded to an interval because weights will stabilize at one of their boundary values (van Rossum et al., 2000; Rubin et al., 2001).”
Does Spike-Timing-Dependent Synaptic Plasticity Couple or Decouple Neurons Firing in Synchrony? (2012, background): “For example, μ= 1 yields a multiplicative rule (Rubin et al., 2001), whereas μ= 0 yields the additive rule of section 4.”
Learning Input Correlations through Nonlinear Temporally Asymmetric Hebbian Plasticity (2003, methodology): “For this input regime, it has been found in an integrate-and-fire neuron that additive learning ( 0) breaks the symmetry of the statistically identical presynaptic inputs and leads to a bimodal weight distribution (Song et al., 2000; Rubin et al., 2001).”
Spike-Timing-Dependent Plasticity in Balanced Random Networks (2007, background): “, 2004) or multiplicative rules (Rubin et al., 2001), or anything in between (Gütig et al.”
SPIKING NEURON MODELS Single Neurons , Populations , Plasticity (2002, methodology): “The choice of how the bounds are implemented turns out to have an important influence on the weight distribution P (w, t) (van Rossum et al., 2000; Rubin et al., 2001).”
Partial Breakdown of Input Specificity of STDP at Individual Synapses Promotes New Learning (2016, methodology): “STDP was implemented as in previous modeling studies (Song et al., 2000; Kempter et al., 2001; Rubin et al., 2001; Song and Abbott, 2001).”
Spike-Timing Dependent Plasticity for Evolved Robots (2002, background): “Additional elements, such as the directional damping of synaptic change (Rubin et al., 2001) or longer-term stabilizing regulation based on postsynaptic activity (Horn, Levy, & Ruppin, 1998; Turrigiano, 1999) may come to the rescue.”