Understanding spike-time-dependent plasticity: A biologically motivated computational model

Long-term synaptic plasticity underlies many important learning processes in the brain. Recent physiological data have shown that the precise relative timings of pre- and post-synaptic neuron firings at a synapse determine both the direction of certain types of modification (potentiation or depression), and magnitude of this modification. We propose a neurophysiological mechanism by which this spike-time-dependent plasticity (STDP) could arise, and support this hypothesis using a model involving calcium dynamics. We show that, in addition to reproducing experimental data for paired spikes, the model can explain differences in experimentally observed STDP forms. We also demonstrate that the model provides a good match to recent data for the triplet and quadruplet paradigms, and that a simulated network of reciprocally connected neurons using this learning rule can store and recall a simple temporal sequence. In conclusion we make predictions from the model both on the plasticity effects of quadruple spike interactions and manipulations of concentrations of components involved at the synapse.