DECODING SYNAPSES

The strength of many synapses is modified by various use and time-depe ndent processes, including facilitation and depression. A general desc ription of synaptic transfer characteristics must account for the hist ory-dependence of synaptic efficacy and should be able to predict the postsynaptic response to any temporal pattern of presynaptic activity. To generate such a description, we use an approach similar to the dec oding method used to reconstruct a sensory input from a neuronal firin g pattern. Specifically, a mathematical fit of the postsynaptic respon se to an isolated action potential is multiplied by an amplitude facto r that depends on a time-dependent function summed over all previous p resynaptic spikes. The amplitude factor is, in general, a nonlinear fu nction of this sum. Approximate forms of the time-dependent function a nd the nonlinearity are extracted from the data, and then both functio ns are constructed more precisely by a learning algorithm. This approa ch, which should be applicable to a wide variety of synapses, is appli ed here to several crustacean neuromuscular junctions, After training on data from random spike sequences, the method predicts the postsynap tic response to an arbitrary train of presynaptic action potentials. U sing a model synapse, we relate the functions used in the fit to under lying biophysical processes. Fitting different neuromuscular junctions allows us to compare their responses to sequences of action potential s and to contrast the time course and degree of facilitation or depres sion that they exhibit.