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*{{Bibitem article 2|Recurrent networks with short term synaptic depression.|Journal of Computational Neuroscience.|27(3)|2009|607-620|York|Lawrence Christopher|van Rossum|Mark C. W.|preprint=[http://dx.doi.org/10.1007/s10827-009-0172-4 doi:10.1007/s10827-009-0172-4]|label=York09|doi=10.1007/s10827-009-0172-4}}

*{{Bibitem article 2|Recurrent networks with short term synaptic depression.|Journal of Computational Neuroscience.|27(3)|2009|607-620|York|Lawrence Christopher|van Rossum|Mark C. W.|preprint=[http://dx.doi.org/10.1007/s10827-009-0172-4 doi:10.1007/s10827-009-0172-4]|label=York09|doi=10.1007/s10827-009-0172-4}}

*{{Bibitem article 2| Short-Term Synaptic Plasticity.|Annual Review of Physiology.|64(1)|2002|355-405|Zucker|Robert S.|Regehr|Wade G.|preprint=[http://dx.doi.org/10.1146/annurev.physiol.64.092501.114547 doi:10.1146/annurev.physiol.64.092501.114547]|label=Zucker02}}

*{{Bibitem article 2| Short-Term Synaptic Plasticity.|Annual Review of Physiology.|64(1)|2002|355-405|Zucker|Robert S.|Regehr|Wade G.|preprint=[http://dx.doi.org/10.1146/annurev.physiol.64.092501.114547 doi:10.1146/annurev.physiol.64.092501.114547]|label=Zucker02}}

 

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此词条由神经动力学读书会词条梳理志愿者（Glh20100487）翻译审校，未经专家审核，带来阅读不便，请见谅

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*1 Phenomenological model

* 1 Phenomenological model

*2 Effects on information transmission

* 2 Effects on information transmission

**2.1 Temporal filtering

**2.1 Temporal filtering

**2.2 Gain control

** 2.2 Gain control

*3 Effects on network dynamics

*3 Effects on network dynamics

**3.1 Prolongation of neural responses to transient inputs

** 3.1 Prolongation of neural responses to transient inputs

**3.2 Modulation of network responses to external input

**3.2 Modulation of network responses to external input

**3.3 Induction of instability or mobility of network state

**3.3 Induction of instability or mobility of network state

** 3.4 Enrichment of attractor dynamics

**3.4 Enrichment of attractor dynamics

*4 Appendix A: Derivation of a temporal filter for short-term depression

*4 Appendix A: Derivation of a temporal filter for short-term depression

*5 References

*5 References

*3对网络动态的影响  

*3对网络动态的影响  

**3.1延长神经对瞬态输入的反应

**3.1延长神经对瞬态输入的反应

**3.2网络对外部输入响应的调制

** 3.2网络对外部输入响应的调制

**3.3网络状态不稳定或迁移的诱导

**3.3网络状态不稳定或迁移的诱导

**3.4吸引子动力学的富集

**3.4吸引子动力学的富集

*4附录A:短期抑郁的时间过滤器的推导

* 4附录A:短期抑郁的时间过滤器的推导

*5引用

*5引用

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Since STP has a much longer time scale than that of single neuron dynamics (the latter is typically in the time order of  milliseconds), a new feature STP can bring to the network dynamics is prolongation of neural responses to a transient input. This stimulus-induced residual activity therefore holds a memory trace of the input, lasting up to several hundred milliseconds in a large-size network, and can serve as a buffer for information processing. For example, it has been shown that STD-mediated residual activity can cause a neural system to discriminate between rhythmic inputs of different periods (Karmorkar 07). STP also plays an important role in a general computation framework called a reservoir network. In this framework, STP, together with other dynamical elements of a large-size network, effectively map the input features from a low-dimensional space to the high-dimensional state space of the network that includes both active (neural) and hidden (synaptic) components, so that the input information can be more easily read out (Buonomano 09). In a recent development it was proposed that STF-enhanced synapses themselves can hold the memory trace of an input without recruiting persistent firing of neurons, potentially providing the most economical and robust way to implement working memory (Mongillo 08).

Since STP has a much longer time scale than that of single neuron dynamics (the latter is typically in the time order of  milliseconds), a new feature STP can bring to the network dynamics is prolongation of neural responses to a transient input. This stimulus-induced residual activity therefore holds a memory trace of the input, lasting up to several hundred milliseconds in a large-size network, and can serve as a buffer for information processing. For example, it has been shown that STD-mediated residual activity can cause a neural system to discriminate between rhythmic inputs of different periods (Karmorkar 07). STP also plays an important role in a general computation framework called a reservoir network. In this framework, STP, together with other dynamical elements of a large-size network, effectively map the input features from a low-dimensional space to the high-dimensional state space of the network that includes both active (neural) and hidden (synaptic) components, so that the input information can be more easily read out (Buonomano 09). In a recent development it was proposed that STF-enhanced synapses themselves can hold the memory trace of an input without recruiting persistent firing of neurons, potentially providing the most economical and robust way to implement working memory (Mongillo 08).

===Modulation of network responses to external input ===

===Modulation of network responses to external input===

Since STP modifies synaptic efficacy instantly, it can modulate the network response to sustained external inputs. An example of this is bursty synchronous firing in an STD-dominated network, either spontaneously or in response to external inputs. The resulting bursts of activity are called population spikes (Loebel 02). To understand this effect, consider a network with strong recurrent interactions between neurons. When a sufficiently large group of neurons fire together, e.g. triggered by external stimulus, they can recruit other neurons via an avalanche-like process. However, after a large synchronous burst of activity, the synapses are weakened by STD, reducing the recurrent currents rapidly, and consequently the network activity returns to baseline. The network will not be activated again until the synapses are sufficiently recovered from depression. Therefore, the rate of population spikes is determined by the time constant of STD (Fig.3A,B). STF can also modulate the network response to external inputs, but in a very different manner (Barak 07). The varied response properties mediated by STP may provide different ways of representing and conveying the stimulus information in a network.

Since STP modifies synaptic efficacy instantly, it can modulate the network response to sustained external inputs. An example of this is bursty synchronous firing in an STD-dominated network, either spontaneously or in response to external inputs. The resulting bursts of activity are called population spikes (Loebel 02). To understand this effect, consider a network with strong recurrent interactions between neurons. When a sufficiently large group of neurons fire together, e.g. triggered by external stimulus, they can recruit other neurons via an avalanche-like process. However, after a large synchronous burst of activity, the synapses are weakened by STD, reducing the recurrent currents rapidly, and consequently the network activity returns to baseline. The network will not be activated again until the synapses are sufficiently recovered from depression. Therefore, the rate of population spikes is determined by the time constant of STD (Fig.3A,B). STF can also modulate the network response to external inputs, but in a very different manner (Barak 07). The varied response properties mediated by STP may provide different ways of representing and conveying the stimulus information in a network.

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*Fung, C. C. Alan; Wong, K. Y. Michael; Wang, He and Wu, Si (2012). Dynamical Synapses Enhance Neural Information Processing: Gracefulness, Accuracy, and Mobility. ''Neural Computation.'' 24(5): 1147-1185. doi:10.1162/neco_a_00269.doi:10.1162/NECO_a_00269

*C. C. Fung, K. Y. Michael Wong and S. Wu. Delay Compensation with Dynamical Synapses. Advances in Neural Information Processing Systems 16, 2012.

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*C. C. A. Fung, H. Wang, K. Lam, K. Y. M. Wong and S. Wu. Resolution enhancement in neural networks with dynamical synapses. Front. Comput. Neurosci. 7:73. doi: 10.3389/fncom.2013.00073, 2013.

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