RT Journal Article
SR Electronic
T1 Stable Hebbian Learning from Spike Timing-Dependent Plasticity
JF The Journal of Neuroscience
JO J. Neurosci.
FD Society for Neuroscience
SP 8812
OP 8821
DO 10.1523/JNEUROSCI.20-23-08812.2000
VO 20
IS 23
A1 van Rossum, M. C. W.
A1 Bi, G. Q.
A1 Turrigiano, G. G.
YR 2000
UL http://www.jneurosci.org/content/20/23/8812.abstract
AB 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.