Synapses with short-term plasticity are optimal estimators of presynaptic membrane potentials.
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 explored the possibility that the neuron's efferent synapses perform the critical computational step of estimating the membrane potential trajectory from the spikes. We found that short-term changes in synaptic efficacy can be interpreted as implementing an optimal estimator of this trajectory. Short-term depression arose when presynaptic spiking was sufficiently intense as to reduce the uncertainty associated with the estimate; short-term facilitation reflected structural features of the statistics of the presynaptic neuron such as up and down states. Our analysis provides a unifying account of a powerful, but puzzling, form of plasticity.
Pfister
Jean-Pascal
JP
Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK. jean-pascal.pfister@eng.cam.ac.uk
Dayan
Peter
P
Lengyel
Máté
M
D000818:Animals; D001703:Biophysics; D003198:Computer Simulation; D008564:Membrane Potentials / Q000502:physiology*; D008959:Models, Neurological; D009473:Neuronal Plasticity / Q000502:physiology*; D009474:Neurons / Q000166:cytology*; D017711:Nonlinear Dynamics; D017729:Presynaptic Terminals / Q000502:physiology*; D013269:Stochastic Processes; D013569:Synapses / Q000502:physiology*