Stable Hebbian Learning from Spike Timing-Dependent Plasticity

By Mark C. W. van Rossum, Guoqiang Bi, Gina G. Turrigiano. 2000

778 citations recorded by the source corpus.

We explore a synaptic plasticity model that incorporates recent findings that potentiation and depression can be induced by precisely timed pairs of synaptic events and postsynaptic spikes. In addition we include the observation that strong synapses undergo relatively less potentiation than weak synapses, whereas depression is independent of synaptic strength. After random stimulation, the synaptic weights reach an equilibrium distribution which is stable, unimodal, and has positive skew. This weight distribution compares favorably to the distributions of quantal amplitudes and of receptor number observed experimentally in central neurons and contrasts to the distribution found in plasticity models without size-dependent potentiation. Also in contrast to those models, which show strong competition between the synapses, stable plasticity is achieved with little competition. Instead, competition can be introduced by including a separate mechanism that scales synaptic strengths multiplicatively as a function of postsynaptic activity. In this model, synaptic weights change in proportion to how correlated they are with other inputs onto the same postsynaptic neuron. These results indicate that stable correlation-based plasticity can be achieved without introducing competition, suggesting that plasticity and competition need not coexist in all circuits or at all developmental stages.

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

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Citations in the corpus

Optimal information storage and the distribution of synaptic weights: perceptron versus Purkinje cell. (2004, background): “…Jean-Pierre Nadal,2 and Boris Barbour4,* 1Neurophysique et Physiologie Université René Descartes 45 rue des Saints Pères timing-dependent synaptic plasticity (van Rossum et al., 2000; Song et al., 2000; Rubin et al., 2001; Cateau and75270 Paris Cedex 06 France Fukai, 2003; Gütig et al., 2003).”
Relating STDP to BCM (2003, background): “The relationship between the two has been explored by, among others, van Rossum et al. (2000), Senn, Markram, & Tsodyks (2001), Kempter, Gerstner , & van Hemmen (1999, 2001), Castellani, Quinlan, Cooper, & Shouval (2001), and Abarbanel, Huerta, & Rabinovich (2002), though no attempts have been to…”
Spiking activity propagation in neuronal networks: reconciling different perspectives on neural coding (2010, background): “Moreover, firing rate and correlation (or synchrony) are attractive candidate neural codes because both can affect network function by influencing synaptic plasticit”
Evolution of Cartesian Genetic Programs for Development of Learning Neural Architecture (2011, methodology): “…of only two neurons, Evolutionary Computation Volume 19, Number 3 479 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/EVCO_a_00043 by guest on 30 May 2021 they can result in interesting patterns of global behavior, including competition between synapses (Van Rossum et al., 2000).”
Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity (2007, background): “Several authors have studied the effect of STDP with Poisson spike trains [4,23].”
The spike timing dependence of plasticity (2012, methodology): “…by strengthening synapses whose activity leads postsynaptic spikes, and weakening synapses whose activity lags postsynaptic spikes, which represent ineffective synapses onto otherwise active neurons (Abbott and Nelson, 2000; Song et al., 2000; van Rossum et al., 2000; Song and Abbott, 2001).”
Stable Learning in Stochastic Network States (2012, methodology): “We compared a weight-dependent STDP rule (van Rossum et al., 2000) and the mSTDP rule in a topological network with lateral propagation at finite speed, displaying an AI regime with a stable synaptic weight distribution for weight-dependent STDP rules.”
Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks V: self-organization schemes and weight dependence (2010, methodology): “This has been previously studied for a single neuron (van Rossum et al. 2000; Gütig et al. 2003; Burkitt et al. 2004).”