14-06-2018 Rui Ponte Costa


Learning probabilistic synapses: where and when


The probabilistic nature of synaptic transmission has remained enigmatic. However, recent developments have started to shed light on where, when and why the brain tunes the statistics of synaptic responses. In this talk I will present a theoretical framework in which long-term plasticity performs an optimization of the postsynaptic response statistics toward a given mean with minimal variance. Consequently, the state of the synapse at the time of plasticity induction (for a given optimization metric) determines the ratio of pre- and postsynaptic modifications -- the key components of probabilistic synaptic responses. We show that our framework can capture the experimentally observed expression loci of the hippocampal and neocortical synaptic potentiation studies we examined. Moreover, the theory predicts presynaptic expression of long-term depression, consistent with several experimental observations. At inhibitory synapses, the theory suggests a statistically efficient excitatory-inhibitory balance in which changes in inhibitory postsynaptic response statistics specifically target the mean excitation. Our results suggest a theory for studying the expression mechanisms of long-term synaptic plasticity, and raises several questions on the function of stochastic synaptic transmission.