Structural contributions to short-term synaptic plasticity.

Matthew A Xu-Friedman; Wade G Regehr

doi:10.1152/physrev.00016.2003

Home/Archive/PubMed

PubMed · 14715911

Structural contributions to short-term synaptic plasticity.

Matthew A Xu-Friedman·Wade G Regehr

View original source ↗Export metadata ↓

Abstract

Synaptic ultrastructure is critical to many basic hypotheses about synaptic transmission. Various aspects of synaptic ultrastructure have also been implicated in the mechanisms of short-term plasticity. These forms of plasticity can greatly affect synaptic strength during ongoing activity. We review the evidence for how synaptic ultrastructure may contribute to facilitation, depletion, saturation, and desensitization.

Explore related subjects

Action Potentials Animals Brain Humans Models, Neurological Neuronal Plasticity Synapses Synaptic Transmission Journal Article Research Support, U.S. Gov't, P.H.S.Review

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Matthew A Xu-Friedman, Wade G Regehr. 2004. Structural contributions to short-term synaptic plasticity.. https://doi.org/10.1152/physrev.00016.2003

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2004

A note on the discrepancy between the predicted and observed speed of the propagated action potential in the squid giant axon.

The Hodgkin-Huxley model for the ionic currents in the membrane of the squid giant axon has become the standard model for the electrophysiological behaviour of many excitable cells. A strong test of the model predicted a travelling wave speed of 18.76 m/s for the propagated action potential in an axon with a reported speed of 21.2 m/s. This discrepancy between prediction and observation was considered satisfactory when the model was proposed 50 years ago, appears not to have been re-evaluated, but is unsatisfactory for a mature and important model. The separate and combined influences of measurement error and biological variability on the discrepancy between prediction and observation are quantified, as is the effect of using of a one-dimensional model to represent a three-dimensional axon. The main tool in this investigation is the use of simulation to study the behaviour of the Hodgkin-Huxley membrane model. These studies show that measurement error in combination with biological variability cannot account for the discrepancy between prediction and observation. Also, calculation shows that the one-dimensional description of the behaviour of the axon is adequate. Further calculation shows that the travelling wave description of the propagated action potential is valid only for sufficiently long axons. In shorter axons the propagated action potential is predicted to travel faster than the travelling wave; consequently under suitable experimental conditions the discrepancy between prediction and observation may be negligible.

Action Potentials↗

PubMed2004

Online prediction of self-paced hand-movements from subthalamic activity using neural networks in Parkinson's disease.

The significance of local field potential (LFP) activity in the subthalamic nucleus (STN) of patients with Parkinson's disease is unclear. Here we show that it is possible to predict self-paced hand-movements from the oscillatory nature of the STN LFP on a trial-by-trial basis. To this end we used a neural networks' classification algorithm on features representing different measures of spectral activity. Our experiments were simulated to process online LFP signal recordings collected beforehand from macroelectrodes implanted in STN. With spectral features extracted via wavelet transformation, we were able to predict a voluntary hand-movement's onset with 95% sensitivity and 77% specificity. Most predictions were made over a second in advance of the movement. We conclude that oscillatory LFP activity in STN is directly or indirectly related to processes involved in motor preparation. The ability to predict movement in real-time may open up several experimental and therapeutic possibilities.

Action Potentials↗

PubMed2004

Neural spike classification using parallel selection of all algorithm parameters.

The Forster-Handwerker template-matching algorithm (J. Neurosci. Methods 31 (1990) 109) classifies neuronal spikes according to three parameters selected by the experimenter prior to running the algorithm. Thousands of different combinations of these parameter values are possible producing hundreds of different classifications for each input file. Using a 40-processor Linux-based parallel computing cluster, we ran their algorithm with an effective sampling of all combinations of parameter values in order to generate a list of the classifications that can be generated by the algorithm. A distance measure was used to quantify the similarity between classifications and then to create a distance table containing entries for the distances between all pairs of classifications. Using a self-organizing neural network (SON) and the distance table we group the classifications by similarity and select the best representative classifications that the Forster-Handwerker algorithm can produce.

Action Potentials↗