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The Journal of Neuroscience, December 1, 2000, 20(23):8812-8821

Stable Hebbian Learning from Spike Timing-Dependent Plasticity

M. C. W. van Rossum¹, G. Q. Bi², and G. G. Turrigiano¹

¹ Brandeis University, Department of Biology, Waltham, Massachusetts 02454-9110, and ² University of California at San Diego, Department of Biology, La Jolla, California 92093-0357

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.

Key words: Hebbian plasticity; synaptic weights; synaptic competition; activity-dependent scaling; temporal learning; stochastic approaches

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Copyright © 2000 Society for Neuroscience  0270-6474/00/20238812-10$05.00/0

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