Synapses with short-term plasticity are optimal estimators of presynaptic membrane potentials

Jean-Pascal Pfister (1) , Peter Dayan (1) , Mate Lengyel (1)

1 Engineering Department

The trajectory of the somatic membrane potential of a cortical neuron exactly reflects the computations performed on its afferent inputs. However, the spikes of such a neuron are a very low dimensional and discrete projection of this continually evolving signal. We explore the possibility that the neuron's efferent synapses perform the critical computational step of estimating the membrane potential trajectory from the spikes. We show that short-term changes in synaptic efficacy can be interpreted as implementing an optimal such estimator. Short-term depression arises when pre-synaptic spiking is sufficiently intense as to reduce the uncertainty associated with the estimate; short-term facilitation reflects structural features of the statistics of the pre-synaptic neuron such as up and down states. Our analysis provides a unifying account of a powerful, but puzzling form of plasticity.

Mots clés: Computational Neuroscience; Synaptic Transmission

Jean-Pascal Pfister, Peter Dayan, Mate Lengyel. Synapses with short-term plasticity are optimal estimators of presynaptic membrane potentials. Nature Neuroscience, 2010, 13 (10), pp.1271. ⟨10.1038/nn.2640⟩. ⟨hal-00578282⟩