Encyclopedia of Computational Neuroscience

Living Edition
| Editors: Dieter Jaeger, Ranu Jung

Short Term Plasticity, Biophysical Models

Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7320-6_358-1

Definition

Short-term plasticity refers to changes in synaptic efficacy in response to presynaptic spiking that persist for a few seconds at most but more often decay over a timescale of a few hundred milliseconds. There are two primary types of short-term plasticity: short-term depression and short-term facilitation. Short-term depression is a reduction in synaptic efficacy that is often, but not always, caused by a transient depletion of neurotransmitter vesicles. Short-term facilitation is an increase in synaptic efficacy often caused by a transient increase in the number of vesicles released by presynaptic action potentials. The detailed biophysical mechanisms underlying synaptic facilitation are discussed in Facilitation, Biophysical Models, and we therefore focus on more phenomenological models of facilitation here.

Detailed Description

Stochastic Models of Short-Term Depression Arising from Neurotransmitter Depletion

Short-term depression is often believed to arise primarily...

Keywords

Depression Dock Zucker 
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References

  1. Chance F, Nelson S, Abbott L (1998) Synaptic depression and the temporal response characteristics of v1 cells. J Neurosci 18:4785PubMedGoogle Scholar
  2. Cook DL, Schwindt PC, Grande LA, Spain WJ (2003) Synaptic depression in the localization of sound. Nature 421:66–70PubMedCrossRefGoogle Scholar
  3. de la Rocha J, Parga N (2005) Short-term synaptic depression causes a non-monotonic response to correlated stimuli. J Neurosci 25:8416–8431PubMedCrossRefGoogle Scholar
  4. Fuhrmann G, Segev I, Markram H, Tsodyks M (2002) Coding of temporal information by activity dependent synapses. J Neurophysiol 87:140PubMedGoogle Scholar
  5. Goldman M (2004) Enhancement of information transmission efficiency by synaptic failures. Neural Comput 16:1137–1162PubMedCrossRefGoogle Scholar
  6. Goldman MS, Maldonado P, Abbott L (2002) Redundancy reduction and sustained ring with stochastic depressing synapses. J Neurosci 22:584–591PubMedGoogle Scholar
  7. Grande LA, Spain WJ (2005) Synaptic depression as a timing device. J Physiol 20:201–210CrossRefGoogle Scholar
  8. Hanson JE, Jaeger D (2002) Short-term plasticity shapes the response to simulated normal and parkinsonian input patterns in the globus pallidus. J Neurosci 22:5164–5172PubMedGoogle Scholar
  9. Lindner B, Ganglo D, Longtin A, Lewis JE (2009) Broadband coding with dynamic synapses. J Neurosci 29:2076–2087PubMedCrossRefGoogle Scholar
  10. Maass W, Zador AM (1999) Dynamic stochastic synapses as computational units. Neural Comput 11:903–917PubMedCrossRefGoogle Scholar
  11. Markram H, Wang Y, Tsodyks M (1998) Differential signaling via the same axon of neocortical pyramidal neurons. Proc Natl Acad Sci U S A 95:5323PubMedCentralPubMedCrossRefGoogle Scholar
  12. Matveev V, Wang XJ (2000) Implications of all-or-none synaptic transmission and short term depression beyond vesicle depletion: a computational study. J Neurosci 20:1575–1588PubMedGoogle Scholar
  13. McDonnell MD, Mohan A, Stricker C (2013) Mathematical analysis and algorithms for efficiently and accurately implementing stochastic simulations of short-term synaptic depression and facilitation. Front Comput Neurosci 7:58PubMedCentralPubMedCrossRefGoogle Scholar
  14. Merkel M, Lindner B (2010) Synaptic filtering of rate-coded information. Phys Rev E 81:041921CrossRefGoogle Scholar
  15. Mohan A, McDonnell MD, Stricker C (2013) Interaction of short-term depression and ring dynamics in shaping single neuron encoding. Front Comput Neurosci 7:41PubMedCentralPubMedCrossRefGoogle Scholar
  16. Pfister JP, Dayan P, Lengyel M (2010) Synapses with short-term plasticity are optimal estimators of presynaptic membrane potentials. Nat Neurosci 13:1271–1275PubMedCentralPubMedCrossRefGoogle Scholar
  17. Reich S, Rosenbaum R (2013) The impact of short term synaptic depression and stochastic vesicle dynamics on neuronal variability. J Comput Neurosci 35:1–15Google Scholar
  18. Rosenbaum R, Rubin J, Doiron B (2012) Short term synaptic depression imposes a frequency dependent filter on synaptic information transfer. PLoS Comput Biol 8:e1002557PubMedCentralPubMedCrossRefGoogle Scholar
  19. Rosenbaum R, Rubin JE, Doiron B (2013) Short-term synaptic depression and stochastic vesicle dynamics reduce and shape neuronal correlations. J Neurophysiol 109:475–484PubMedCentralPubMedCrossRefGoogle Scholar
  20. Rosenbaum R, Zimnik A, Zheng F, Turner RS, Alzheimer C, Doiron B, Rubin JE (2014) Axonal and synaptic failure suppress the transfer of ring rate oscillations, synchrony and information during high frequency deep brain stimulation. Neurobiol Dis 62:86–99PubMedCrossRefGoogle Scholar
  21. Rothman JS, Cathala L, Steuber V, Silver RA (2009) Synaptic depression enables neuronal gain control. Nature 457:1015–1018PubMedCentralPubMedCrossRefGoogle Scholar
  22. Scott P, Cowan AI, Stricker C (2012) Quantifying impacts of short-term plasticity on neuronal information transfer. Phys Rev E 85:041921CrossRefGoogle Scholar
  23. Tsodyks MV, Markram H (1997) The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. Proc Natl Acad Sci U S A 94:719–723PubMedCentralPubMedCrossRefGoogle Scholar
  24. Tsodyks M, Pawelzik K, Markram H (1998) Neural networks with dynamic synapses. Neural Comput 10:821–835PubMedCrossRefGoogle Scholar
  25. Varela JA, Sen K, Gibson J, Fost J, Abbott LF et al (1997) A quantitative description of short-term plasticity at excitatory synapses in layer 2/3 of rat primary visual cortex. J Neurosci 17:7926–7940PubMedGoogle Scholar
  26. Vere-Jones D (1966) Simple stochastic models for the release of quanta of transmitter from a nerve terminal. Aust NZ J Stat 8:53–63CrossRefGoogle Scholar
  27. Wang XJ (1999) Fast burst ring and short-term synaptic plasticity: a model of neocortical chattering neurons. J Neurosci 89:347–362Google Scholar
  28. Wong AY, Graham BP, Billups B, Forsythe ID (2003) Distinguishing between presynaptic and postsynaptic mechanisms of short-term depression during action potential trains. J Neurosci 23:4868–4877PubMedGoogle Scholar
  29. Xu J, Wu LG (2005) The decrease in the presynaptic calcium current is a major cause of short-term depression at a calyx-type synapse. Neuron 46:633–645PubMedCrossRefGoogle Scholar
  30. Zador A (1998) Impact of synaptic unreliability on the information transmitted by spiking neurons. J Neurophysiol 79:1219PubMedGoogle Scholar
  31. Zucker R, Regehr W (2002) Short-term synaptic plasticity. Annu Rev Physiol 64:355–405PubMedCrossRefGoogle Scholar

Further Reading

  1. Branco T, Staras K (2009) The probability of neurotransmitter release: variability and feedback control at single synapses. Nat Rev Neurosci 10:373–383PubMedCrossRefGoogle Scholar
  2. Senn W, Markram H, Tsodyks M (2001) An algorithm for modifying neurotransmitter release probability based on pre- and postsynaptic spike timing. Neural Comput 13:35–67PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Applied and Computational Mathematics and StatisticsUniversity of Notre DameNotre DameUSA