Reducing Spike Train Variability: A Computational Theory of Spike-Timing Dependent Plasticity

Abstract

Experimental studies have observed synaptic potentiation when a presynaptic neuron fires shortly before a postsynaptic neuron, and synaptic depression when the presynaptic neuron fires shortly af- ter. The dependence of synaptic modulation on the precise tim- ing of the two action potentials is known as spike-timing depen- dent plasticity or STDP. We derive STDP from a simple compu- tational principle: synapses adapt so as to minimize the postsy- naptic neuron's variability to a given presynaptic input, causing the neuron's output to become more reliable in the face of noise. Using an entropy-minimization objective function and the biophys- ically realistic spike-response model of Gerstner (2001), we simu- late neurophysiological experiments and obtain the characteristic STDP curve along with other phenomena including the reduction in synaptic plasticity as synaptic efficacy increases. We compare our account to other efforts to derive STDP from computational princi- ples, and argue that our account provides the most comprehensive coverage of the phenomena. Thus, reliability of neural response in the face of noise may be a key goal of cortical adaptation.