Decoding Synapses

Abstract

The strength of many synapses is modified by various use and time-dependent processes, including facilitation and depression. A general description of synaptic transfer characteristics must account for the history-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 decoding method used to reconstruct a sensory input from a neuronal firing pattern. Specifically, a mathematical fit of the postsynaptic response to an isolated action potential is multiplied by an amplitude factor that depends on a time-dependent function summed over all previous presynaptic spikes. The amplitude factor is, in general, a nonlinear function of this sum. Approximate forms of the time-dependent function and the nonlinearity are extracted from the data, and then both functions are constructed more precisely by a learning algorithm. This approach, which should be applicable to a wide variety of synapses, is applied here to several crustacean neuromuscular junctions. After training on data from random spike sequences, the method predicts the postsynaptic response to an arbitrary train of presynaptic action potentials. Using a model synapse, we relate the functions used in the fit to underlying biophysical processes. Fitting different neuromuscular junctions allows us to compare their responses to sequences of action potentials and to contrast the time course and degree of facilitation or depression that they exhibit.

Augmentation Increases Vesicular Release Probability in the Presence of Masking Depression at the Frog Neuromuscular Junction

“…7D), suggesting an additional faster depression (Dobrunz et al, 1997;Varela et al, 1997), or that factors in addition to depletion may be contributing to depression (Zucker and Regehr, 2002). A third approach that will be important to quantify dynamic changes in the components is modeling of the release process Sen et al, 1996;Varela et al, 1997;Dittman et al, 2000;Matveev et al, 2002;Aristizabal and Glavinovic, 2003).…”

Locus of frequency‐dependent depression identified with multiple‐probability fluctuation analysis at rat climbing fibre‐Purkinje cell synapses

“…Thus, it is possible that presynaptic depression at the CF synaptic connection may be acting as a low-pass filter. Recent modelling studies indicate that temporal changes in release probability can shape the frequency-dependent transmission properties at central connections (Sen et al 1996;Tsodyks & Markram, 1997). The unusual temporal characteristics of release probability at the CF terminal may therefore have an important influence on output coding of the cerebellum.…”

“…Understanding this dynamic behaviour is central to understanding transmission, since synaptic efficacy, at any given time, is dependent on the recent history of the input. Furthermore, modelling studies have shown that the temporal behaviour of synaptic efficacy (the tendency to exhibit short-term facilitation or depression) is important in determining the pattern of frequency-coded information that is transmitted to the postsynaptic cell (Sen, Jorge-Rivera, Marder & Abbott, 1996;Tsodyks & Markram, 1997). Since the early work of del Castillo & Katz (1954b) at the NMJ, synaptic depression is generally assumed to have a presynaptic locus.…”

Synaptic activity slows vesicular replenishment at excitatory synapses of rat hippocampus

“…Estimating the replenishment rate is difficult. In previous studies the assumption was made that the pool sizes are constant (Wu and Betz 1998) and that the replenishment rate remains constant with stimulation (Dittman and Regehr 1998;Sen et al 1996), but the validity of these assumptions is problematic.…”