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When an action potential is transmitted to a postsynaptic neuron, a smallchange in the postsynaptic neuron's membrane potential occurs. These smallchanges, known as a postsynaptic potentials (PSPs), are highly variable, andcurrent models assume that this variability is corrupting noise. In contrast,we show that this variability could have an important computational role:representing a synapse's uncertainty about the optimal synaptic weight (i.e.the best possible setting for the synaptic weight). We show that this linkbetween uncertainty and variability, that we call synaptic sampling, leads tomore accurate estimates of the uncertainty in task relevant quantities, leadingto more effective decision making. Synaptic sampling makes four predictions,all of which have some experimental support. First the more variable a synapseis, the more it should change during LTP protocols. Second, variability shouldincrease as the presynpatic firing rate falls. Third, PSP variance should beproportional to PSP mean. Fourth, variability should increase with distancefrom the cell soma. We provide support for the first two predictions byreanalysing existing datasets, and we find preexisting data in support of thelast two predictions.

Synaptic sampling: A connection between PSP variability and uncertainty explains neurophysiological observations