Computational model of the effects of stochastic conditioning on the induction of long-term potentiation and depression

The long-term potentiation (LTP) or longterm depression (LTD) of synaptic s trength are currently considered to be the first microscopic steps leading to learning and memory. The great majority of experiments (both in vitro an d in vivo) studying the basic mechanisms of LTP and LTD induction use condi tioning protocols in which the presynaptic stimuli are delivered at constan t frequencies. This is not, however, what is commonly found in vivo, where a highly irregular spiking activity seems to drive most of the neuronal fun ctions. Thus, some important aspects of the induction characteristics of LT P and LTD expressed in vivo might have been overlooked by the experiments. Using a simple schematic model for a synapse we show here that, in fact, th e statistical properties of a presynaptic conditioning signal could change the probability to induce LTP and/or LTD, suggesting a new and faster opera ting mode for a synapse.