Stable Hebbian learning from spike timing-dependent plasticity

We explore a synaptic plasticity model that incorporates recent findings th at potentiation and depression can be induced by precisely timed pairs of s ynaptic events and postsynaptic spikes. In addition we include the observat ion that strong synapses undergo relatively less potentiation than weak syn apses, 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 compar es favorably to the distributions of quantal amplitudes and of receptor num ber observed experimentally in central neurons and contrasts to the distrib ution found in plasticity models without size-dependent potentiation. Also in contrast to those models, which show strong competition between the syna pses, stable plasticity is achieved with little competition. Instead, compe tition can be introduced by including a separate mechanism that scales syna ptic strengths multiplicatively as a function of postsynaptic activity. In this model, synaptic weights change in proportion to how correlated they ar e with other inputs onto the same postsynaptic neuron. These results indica te that stable correlation-based plasticity can be achieved without introdu cing competition, suggesting that plasticity and competition need not coexi st in all circuits or at all developmental stages.