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短期突触可塑性 (查看源代码)

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* J. S. Dittman, A. C. Kreitzer and W. G. Regehr. Interplay between facilitation, depression, and residual calcium at three presynaptic terminals. J. Neurosci. 20: 1374-1385, 2000.

*{{Bibitem article 2|Short-term synaptic plasticity as a temporal filter.|Trends in Neurosciences.|24(7)|2001|381-385|Fortune|Eric S.|Rose|Gary J.|preprint=[http://dx.doi.org/10.1016/S0166-2236(00)01835-X doi:10.1016/S0166-2236(00)01835-X]|label=Fortune01|doi=10.1016/s0166-2236(00)01835-x}}

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* G. Fuhrmann et al. Coding of Temporal Information by Activity-Dependent Synapses. J. Neurophysiol. 87: 140-148, 2002.

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* G. Fuhrmann et al. Coding of Temporal Information by Activity-Dependent Synapses. J. Neurophysiol. 87: 140-148, 2002.

*{{Bibitem article 4|Dynamical Synapses Enhance Neural Information Processing: Gracefulness, Accuracy, and Mobility.|Neural Computation.|24(5)|2012|1147-1185|Fung|C. C. Alan|Wong|K. Y. Michael|Wang|He|Wu|Si|preprint=[http://dx.doi.org/10.1162/NECO_a_00269 doi:10.1162/NECO_a_00269]|label=Fung12a|doi=10.1162/neco_a_00269}}

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* 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. 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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* 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.

*{{Bibitem article 2|Neuron Activity Related to Short-Term Memory.|Science.|173(3997)|1971|652-654|Fuster|J. M.|Alexander|G. E.|preprint=[http://dx.doi.org/10.1126/science.173.3997.652 doi:10.1126/science.173.3997.652]|label=Fuster71|doi=10.1126/science.173.3997.652}}

*{{Bibitem article 3 |Redundancy Reduction and Sustained Firing with Stochastic Depressing Synapses|The Journal of Neuroscience|22(2)|2002|584-591|Goldman|Mark S.|Maldonado|Pedro|Abbott|L. F.|label=Goldman02}}

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*{{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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~~[[Category:Neuroscience]]~~

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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~~

=Short-term synaptic plasticity=

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*1 Phenomenological model

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* 2 Effects on information transmission

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*2 Effects on information transmission

**2.1 Temporal filtering

**2.2 Gain control

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**3.2 Modulation of network responses to external input

**3.3 Induction of instability or mobility of network state

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**3.4 Enrichment of attractor dynamics

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** 3.4 Enrichment of attractor dynamics

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

*5 References

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**2.1时间过滤

**2.2增益控制

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* 3对网络动态的影响

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*3对网络动态的影响

**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

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which is shown in Fig. 2A,B. In particular, for depression-dominated synapses (), the average synaptic efficacy decays inversely with the rate, and the stationary synaptic current saturates at the limiting frequency , above which dynamic synapses cannot transmit information about the stationary firing rate (Fig. 2A). On the other hand, facilitating synapses can be tuned for a particular presynaptic rate that depends on STP parameters (Fig. 2B).

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===Temporal filtering ===

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===Temporal filtering===

The above analysis only describes neural population firing with stationary firing rates. Eq. (3) can be used to derive the filtering properties of dynamic synapses when the presynaptic population firing rate changes arbitrarily with time. In Appendix A we present the corresponding calculation for depression-dominated synapses (). By considering small perturbations with around the constant rate , the Fourier transform of the synaptic current is approximated by

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通过将STD和STF相结合，可以进一步提高神经信息的传输。例如，通过结合stf主导的兴奋性突触和std主导的抑制性突触，可以增强突触后神经元对高频时代的检测(Klyachko 06)。在接受std为主和stf为主输入的突触后神经元中，神经反应可以显示低通和高通滤波特性(Fortune 01)。

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=== Gain control===

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===Gain control===

Since STD suppresses synaptic efficacy in a frequency-dependent manner, it has been suggested that STD provides an automatic mechanism to achieve gain control, namely, by assigning high gain to slowly firing afferents and low gain to rapidly firing afferents (Abbott 97, Abbott 04, Cook 03). If a steady presynaptic firing rate changes abruptly by an amount , the first spike at the new rate will be transmitted with the efficacy before the synapse is further depressed. Thus, the transient increase in synaptic input will be proportional to , which is approximately proportional to for large rates (see above). This is reminiscent of Weber’s law, which states that a transient synaptic response is roughly proportional to the percentage change of the input firing rate. Fig. 2D shows that for a fixed-size rate change , the response decreases as a function of the steady input value; whereas without STD, the response would be constant for a fixed-size rate change.

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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).

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

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===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.

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*Fortune, Eric S. and Rose, Gary J. (2001). Short-term synaptic plasticity as a temporal filter. ''Trends in Neurosciences.'' 24(7): 381-385. doi:10.1016/s0166-2236(00)01835-x.doi:10.1016/S0166-2236(00)01835-X

*G. Fuhrmann et al. Coding of Temporal Information by Activity-Dependent Synapses. J. Neurophysiol. 87: 140-148, 2002.

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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

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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.

*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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*Klyachko, Vitaly A. and Stevens, Charles F. (2006). Excitatory and Feed-Forward Inhibitory Hippocampal Synapses Work Synergistically as an Adaptive Filter of Natural Spike Trains. ''PLoS Biology.'' 4(7): e207. doi:10.1371/journal.pbio.0040207.doi:10.1371/journal.pbio.0040207

*A. Loebel and M. Tsodyks. Computation by ensemble synchronization in recurrent networks with synaptic depression. J. Comput. Neurosci. 13: 111-124, 2002.

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*Markram, H.; Wang, Y. and Tsodyks, M. (1998). Differential signaling via the same axon of neocortical pyramidal neurons. ''Proceedings of the National Academy of Sciences.'' 95(9): 5323-5328. doi:10.1073/pnas.95.9.5323.doi:10.1073/pnas.95.9.5323

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* Markram, H.; Wang, Y. and Tsodyks, M. (1998). Differential signaling via the same axon of neocortical pyramidal neurons. ''Proceedings of the National Academy of Sciences.'' 95(9): 5323-5328. doi:10.1073/pnas.95.9.5323.doi:10.1073/pnas.95.9.5323

*Markram, Henry and Tsodyks, Misha (1996). Redistribution of synaptic efficacy between neocortical pyramidal neurons. ''Nature.'' 382(6594): 807-810. doi:10.1038/382807a0.doi:10.1038/382807a0

*Mejías, Jorge F. and Torres, Joaquín J. (2008). The role of synaptic facilitation in spike coincidence detection. ''Journal of Computational Neuroscience.'' 24(2): 222-234. doi:10.1007/s10827-007-0052-8.doi:10.1007/s10827-007-0052-8

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