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Short-term plasticity (STP) ([[#Stevens95|Stevens 95]], [[#Markram96|Markram 96]], [[#Abbott97|Abbott 97]], [[#Zucker02|Zucker 02]], [[#Abbott04|Abbott 04]]), also called dynamical synapses, refers to a phenomenon in which synaptic efficacy changes over time in a way that reflects the history of presynaptic activity. Two types of STP, with opposite effects on synaptic efficacy, have been observed in experiments. They are known as Short-Term Depression (STD) and Short-Term Facilitation (STF). STD is caused by depletion of neurotransmitters consumed during the synaptic signaling process at the axon terminal of a pre-synaptic neuron, whereas STF is caused by influx of calcium into the axon terminal after spike generation, which increases the release probability of neurotransmitters. STP has been found in various cortical regions and exhibits great diversity in properties ([[#Markram98|Markram 98]], [[#Dittman00|Dittman 00]], [[#Wang06|Wang 06]]). Synapses in different cortical areas can have varied forms of plasticity, being either STD-dominated, STF-dominated, or showing a mixture of both forms.

Short-term plasticity (STP) ([[#Stevens95|Stevens 95]], [[#Markram96|Markram 96]], [[#Abbott97|Abbott 97]], [[#Zucker02|Zucker 02]], [[#Abbott04|Abbott 04]]), also called dynamical synapses, refers to a phenomenon in which synaptic efficacy changes over time in a way that reflects the history of presynaptic activity. Two types of STP, with opposite effects on synaptic efficacy, have been observed in experiments. They are known as Short-Term Depression (STD) and Short-Term Facilitation (STF). STD is caused by depletion of neurotransmitters consumed during the synaptic signaling process at the axon terminal of a pre-synaptic neuron, whereas STF is caused by influx of calcium into the axon terminal after spike generation, which increases the release probability of neurotransmitters. STP has been found in various cortical regions and exhibits great diversity in properties ([[#Markram98|Markram 98]], [[#Dittman00|Dittman 00]], [[#Wang06|Wang 06]]). Synapses in different cortical areas can have varied forms of plasticity, being either STD-dominated, STF-dominated, or showing a mixture of both forms.

短期可塑性 (STP) ([[#Stevens95|Stevens 95]], [[#Markram96|Markram 96]], [[#Abbott97|Abbott 97]], [[#Zucker02|Zucker 02]], [[#Abbott04|Abbott 04]])，也称为动态突触，是指突触功效随时间以反映突触前活动历史的方式变化的现象 . 在实验中观察到两种对突触功效具有相反影响的 STP。 它们被称为短期抑郁症（STD）和短期促进（STF）。 STD 是由突触前神经元轴突末端的突触信号传导过程中消耗的神经递质消耗引起的，而 STF 是由尖峰产生后钙流入轴突末端引起的，这增加了神经递质的释放概率。 STP 已在不同的皮层区域发现并表现出极大的多样性（Markram 98、Dittman 00、Wang 06）。 不同皮层区域的突触可以具有不同形式的可塑性，要么以 STD 为主，要么以 STF 为主，或显示两种形式的混合。

短期可塑性 (STP) ([[#Stevens95|Stevens 95]], [[#Markram96|Markram 96]], [[#Abbott97|Abbott 97]], [[#Zucker02|Zucker 02]], [[#Abbott04|Abbott 04]])，也称为动态突触，是指突触功效随时间以反映突触前活动历史的方式变化的现象 . 在实验中观察到两种对突触功效具有相反影响的 STP。 它们被称为短期抑郁症（STD）和短期促进（STF）。 STD 是由突触前神经元轴突末端的突触信号传导过程中消耗的神经递质消耗引起的，而 STF 是由尖峰产生后钙流入轴突末端引起的，这增加了神经递质的释放概率。 STP 已在不同的皮层区域发现并表现出极大的多样性 ([[#Markram98|Markram 98]], [[#Dittman00|Dittman 00]], [[#Wang06|Wang 06]])。 不同皮层区域的突触可以具有不同形式的可塑性，要么以 STD 为主，要么以 STF 为主，或显示两种形式的混合。

Compared with long-term plasticity ([[#Bi01|Bi 01]]), which is hypothesized as the neural substrate for experience-dependent modification of neural circuit, STP has a shorter time scale, typically on the order of hundreds to thousands of milliseconds.  The modification it induces to synaptic efficacy is temporary.  Without continued presynaptic activity, the synaptic efficacy will quickly return to its baseline level.

Compared with long-term plasticity ([[#Bi01|Bi 01]]), which is hypothesized as the neural substrate for experience-dependent modification of neural circuit, STP has a shorter time scale, typically on the order of hundreds to thousands of milliseconds.  The modification it induces to synaptic efficacy is temporary.  Without continued presynaptic activity, the synaptic efficacy will quickly return to its baseline level.

与长期可塑性（Bi 01）相比，STP 具有更短的时间尺度，通常为数百到数千毫秒。 它对突触功效的改变是暂时的。 如果没有持续的突触前活动，突触功效将迅速恢复到其基线水平。

与长期可塑性 ([[#Bi01|Bi 01]])相比，STP 具有更短的时间尺度，通常为数百到数千毫秒。 它对突触功效的改变是暂时的。 如果没有持续的突触前活动，突触功效将迅速恢复到其基线水平。

Although STP appears to be an unavoidable consequence of synaptic physiology, theoretical studies suggest that its role in brain functions can be profound (see, e.g., publications in ([[#ResearchTopic|Research Topic]]) and the references therein). From a computational point of view, the time scale of STP lies between fast neural signaling (on the order of milliseconds) and experience-induced learning (on the order of minutes or more).  This is the time scale of many processes that occur in daily life, for example motor control, speech recognition and working memory. It is therefore plausible that STP might serve as a neural substrate for processing of temporal information on the relevant time scales. STP implies that the response of a post-synaptic neuron depends of the history of presynaptic activity, creating information that in principle can be extracted and used. In a large-size network, STP can greatly enrich the network's dynamical behaviors, endowing the neural system with information processing capacities that would be difficult to implement using static connections.  These possibilities have led to significant interest in the computational functions of STP within the field of Computational Neuroscience.

Although STP appears to be an unavoidable consequence of synaptic physiology, theoretical studies suggest that its role in brain functions can be profound (see, e.g., publications in ([[#ResearchTopic|Research Topic]]) and the references therein). From a computational point of view, the time scale of STP lies between fast neural signaling (on the order of milliseconds) and experience-induced learning (on the order of minutes or more).  This is the time scale of many processes that occur in daily life, for example motor control, speech recognition and working memory. It is therefore plausible that STP might serve as a neural substrate for processing of temporal information on the relevant time scales. STP implies that the response of a post-synaptic neuron depends of the history of presynaptic activity, creating information that in principle can be extracted and used. In a large-size network, STP can greatly enrich the network's dynamical behaviors, endowing the neural system with information processing capacities that would be difficult to implement using static connections.  These possibilities have led to significant interest in the computational functions of STP within the field of Computational Neuroscience.

尽管 STP 似乎是突触生理学的一个不可避免的结果，但理论研究表明它在大脑功能中的作用可能是深远的（例如，参见（研究主题）中的出版物和其中的参考文献）。从计算的角度来看，STP 的时间尺度介于快速神经信号（毫秒级）和经验诱导学习（分钟级或更长时间）之间。这是日常生活中许多过程的时间尺度，例如运动控制、语音识别和工作记忆。因此，STP 可能作为处理相关时间尺度上的时间信息的神经基质是合理的。 STP 意味着突触后神经元的反应取决于突触前活动的历史，从而产生原则上可以提取和使用的信息。在大型网络中，STP 可以极大地丰富网络的动态行为，赋予神经系统以静态连接难以实现的信息处理能力。这些可能性引起了计算神经科学领域对 STP 计算功能的极大兴趣。

尽管 STP 似乎是突触生理学的一个不可避免的结果，但理论研究表明它在大脑功能中的作用可能是深远的（例如，参见（[[#ResearchTopic|Research Topic]]）中的出版物和其中的参考文献）。从计算的角度来看，STP 的时间尺度介于快速神经信号（毫秒级）和经验诱导学习（分钟级或更长时间）之间。这是日常生活中许多过程的时间尺度，例如运动控制、语音识别和工作记忆。因此，STP 可能作为处理相关时间尺度上的时间信息的神经基质是合理的。 STP 意味着突触后神经元的反应取决于突触前活动的历史，从而产生原则上可以提取和使用的信息。在大型网络中，STP 可以极大地丰富网络的动态行为，赋予神经系统以静态连接难以实现的信息处理能力。这些可能性引起了计算神经科学领域对 STP 计算功能的极大兴趣。

==现象学模型Phenomenological model==

==现象学模型Phenomenological model==

The biophysical processes underlying STP are complex. Studies of the computational roles of STP have relied on the creation of simplified phenomenological models ([[#Abbott97|Abbott 97]],[[#Markram98|Markram 98]],[[#Tsodyks98|Tsodyks 98]]).

The biophysical processes underlying STP are complex. Studies of the computational roles of STP have relied on the creation of simplified phenomenological models ([[#Abbott97|Abbott 97]],[[#Markram98|Markram 98]],[[#Tsodyks98|Tsodyks 98]]).

STP 背后的生物物理过程很复杂。 对 STP 计算作用的研究依赖于创建简化的现象学模型（Abbott 97，Markram 98，Tsodyks 98）。

STP 背后的生物物理过程很复杂。 对 STP 计算作用的研究依赖于创建简化的现象学模型 ([[#Abbott97|Abbott 97]],[[#Markram98|Markram 98]],[[#Tsodyks98|Tsodyks 98]])。

In the model proposed by Tsodyks and Markram ([[#Tsodyks98|Tsodyks 98]]), the STD effect is modeled by a normalized variable <math>x</math> (<math>0\leq x \leq1</math>), denoting the fraction of resources that remain available after neurotransmitter depletion. The STF effect is modeled by a utilization parameter <math>u</math>, representing the fraction of available resources ready for use (release probability). Following a spike, (i) <math>u</math> increases due to spike-induced calcium influx to the presynaptic terminal, after which (ii) a fraction <math>u</math> of available resources is consumed to produce the post-synaptic current. Between spikes, <math>u</math> decays back to zero with time constant <math>\tau_f</math> and <math>x</math> recovers to 1 with time constant <math>\tau_d </math>. In summary, the dynamics of STP is given by

In the model proposed by Tsodyks and Markram ([[#Tsodyks98|Tsodyks 98]]), the STD effect is modeled by a normalized variable <math>x</math> (<math>0\leq x \leq1</math>), denoting the fraction of resources that remain available after neurotransmitter depletion. The STF effect is modeled by a utilization parameter <math>u</math>, representing the fraction of available resources ready for use (release probability). Following a spike, (i) <math>u</math> increases due to spike-induced calcium influx to the presynaptic terminal, after which (ii) a fraction <math>u</math> of available resources is consumed to produce the post-synaptic current. Between spikes, <math>u</math> decays back to zero with time constant <math>\tau_f</math> and <math>x</math> recovers to 1 with time constant <math>\tau_d </math>. In summary, the dynamics of STP is given by

在 Tsodyks 和 Markram (Tsodyks 98) 提出的模型中，STD 效应由归一化变量 <math>x</math> (<math>0\leq x \leq1</math>），表示在神经递质耗尽后仍然可用的资源比例。 STF 效应由利用率参数 建模，表示可供使用的可用资源的比例（释放概率）。 在一个尖峰之后，(i)由于尖峰诱导的钙流入突触前末端而增加，之后 (ii) 一小部分<math>u</math> 的可用资源被消耗以产生突触后电流。 在尖峰之间，<math>u</math>衰减回零，时间常数为 <math>\tau_f</math>和 <math>x</math>恢复到 1 具有时间常数 <math>\tau_d </math>。 总之，STP 的动态由下式给出

在 Tsodyks 和 Markram([[#Tsodyks98|Tsodyks 98]])提出的模型中，STD 效应由归一化变量 <math>x</math> (<math>0\leq x \leq1</math>），表示在神经递质耗尽后仍然可用的资源比例。 STF 效应由利用率参数 建模，表示可供使用的可用资源的比例（释放概率）。 在一个尖峰之后，(i)由于尖峰诱导的钙流入突触前末端而增加，之后 (ii) 一小部分<math>u</math> 的可用资源被消耗以产生突触后电流。 在尖峰之间，<math>u</math>衰减回零，时间常数为 <math>\tau_f</math>和 <math>x</math>恢复到 1 具有时间常数 <math>\tau_d </math>。 总之，STP 的动态由下式给出

:<math>\begin{aligned}

:<math>\begin{aligned}

Because STP modifies synaptic efficacy based on the history of presynaptic activity, it can alter neural information transmission ([[#Abbott97|Abbott 97]], [[#Tsodyks97|Tsodyks 97]], [[#Fuhrmann02|Fuhrmann 02]], [[#Rotman11|Rotman 11]], [[#Rosenbaum12|Rosenbaum 12]]). In general, an STD-dominated synapse favors information transfer for low firing rates, since high-frequency spikes rapidly deactivate the synapse. An STF-dominated synapse, however, tends to optimize information transfer for high-frequency bursts, which increase the synaptic strength.  

Because STP modifies synaptic efficacy based on the history of presynaptic activity, it can alter neural information transmission ([[#Abbott97|Abbott 97]], [[#Tsodyks97|Tsodyks 97]], [[#Fuhrmann02|Fuhrmann 02]], [[#Rotman11|Rotman 11]], [[#Rosenbaum12|Rosenbaum 12]]). In general, an STD-dominated synapse favors information transfer for low firing rates, since high-frequency spikes rapidly deactivate the synapse. An STF-dominated synapse, however, tends to optimize information transfer for high-frequency bursts, which increase the synaptic strength.  

因为 STP 根据突触前活动的历史来修改突触功效，所以它可以改变神经信息传递（Abbott 97、Tsodyks 97、Fuhrmann 02、Rotman 11、Rosenbaum 12）。 一般来说，以 STD 为主的突触有利于低发射率的信息传递，因为高频尖峰会迅速使突触失活。 然而，以 STF 为主的突触倾向于优化高频突发的信息传递，从而增加突触强度。  

因为 STP 根据突触前活动的历史来修改突触功效，所以它可以改变神经信息传递([[#Abbott97|Abbott 97]], [[#Tsodyks97|Tsodyks 97]], [[#Fuhrmann02|Fuhrmann 02]], [[#Rotman11|Rotman 11]], [[#Rosenbaum12|Rosenbaum 12]])。 一般来说，以 STD 为主的突触有利于低发射率的信息传递，因为高频尖峰会迅速使突触失活。 然而，以 STF 为主的突触倾向于优化高频突发的信息传递，从而增加突触强度。  

Firing-rate-dependent transmission via dynamic synapses can be analyzed by examining the transmission of uncorrelated Poisson spike trains from a large neuronal population with global firing rate <math>R(t)</math>. The time evolution for the postsynaptic current <math>I(t)</math> can be obtained by averaging Eq. \ref{model} over different realization of Poisson processes corresponding to different spike trains ([[#Tsodyks98|Tsodyks 98]]):

Firing-rate-dependent transmission via dynamic synapses can be analyzed by examining the transmission of uncorrelated Poisson spike trains from a large neuronal population with global firing rate <math>R(t)</math>. The time evolution for the postsynaptic current <math>I(t)</math> can be obtained by averaging Eq. \ref{model} over different realization of Poisson processes corresponding to different spike trains ([[#Tsodyks98|Tsodyks 98]]):

可以通过检查来自具有全局放电率 [math]\displaystyle{ R(t) }[/math] 的大型神经元群体的不相关 Poisson 尖峰序列的传输来分析通过动态突触的放电率依赖性传输。 突触后电流 [math]\displaystyle{ I(t) }[/math] 的时间演化可以通过对等式求平均来获得。 \ref{model} 对应于不同尖峰序列的泊松过程的不同实现（Tsodyks 98）：

可以通过检查来自具有全局放电率 [math]\displaystyle{ R(t) }[/math] 的大型神经元群体的不相关 Poisson 尖峰序列的传输来分析通过动态突触的放电率依赖性传输。 突触后电流 [math]\displaystyle{ I(t) }[/math] 的时间演化可以通过对等式求平均来获得。 \ref{model} 对应于不同尖峰序列的泊松过程的不同实现([[#Tsodyks98|Tsodyks 98]])：

:<math>\begin{aligned}

:<math>\begin{aligned}

where again <math>u^+ = u^- + U(1-u^-)</math> and we neglect time scales on the order of the synaptic time constant. For the stationary rate, <math>R(t) \equiv R_0</math>, we obtain

where again <math>u^+ = u^- + U(1-u^-)</math> and we neglect time scales on the order of the synaptic time constant. For the stationary rate, <math>R(t) \equiv R_0</math>, we obtain

其中 [math]\displaystyle{ u^+ = u^- + U(1-u^-) }[/math] 我们忽略了突触时间常数阶的时间尺度。 对于固定速率，[math]\displaystyle{ R(t) \equiv R_0 }[/math]，我们得到

其中<math>u^+ = u^- + U(1-u^-)</math>我们忽略了突触时间常数阶的时间尺度。 对于固定速率，<math>R(t) \equiv R_0</math>，我们得到

:<math>\begin{aligned}

:<math>\begin{aligned}

which is shown in Fig. 2A,B. In particular, for depression-dominated synapses (<math>u^+ \approx U</math>), the average synaptic efficacy <math>E=Au^+x</math> decays inversely with the rate, and the stationary synaptic current saturates at the limiting frequency <math>\lambda \sim \frac{1}{U\tau_d}</math>, 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).

which is shown in Fig. 2A,B. In particular, for depression-dominated synapses (<math>u^+ \approx U</math>), the average synaptic efficacy <math>E=Au^+x</math> decays inversely with the rate, and the stationary synaptic current saturates at the limiting frequency <math>\lambda \sim \frac{1}{U\tau_d}</math>, 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).

如图 2A、B 所示。 特别是，对于抑郁为主的突触 ([math]\displaystyle{ u^+ \approx U }[/math])，平均突触效能 [math]\displaystyle{ E=Au^+x }[/math] 衰减 与速率成反比，静态突触电流在极限频率 [math]\displaystyle{ \lambda \sim \frac{1}{U\tau_d} }[/math] 处饱和，高于该频率的动态突触不能传输有关 固定发射率（图 2A）。 另一方面，促进突触可以针对取决于 STP 参数的特定突触前速率进行调整（图 2B）。

如图 2A、B 所示。 特别是，对于抑郁为主的突触 <math>u^+ \approx U</math>，平均突触效能<math>E=Au^+x</math>衰减 与速率成反比，静态突触电流在极限频率<math>\lambda \sim \frac{1}{U\tau_d}</math>处饱和，高于该频率的动态突触不能传输有关 固定发射率（图 2A）。 另一方面，促进突触可以针对取决于 STP 参数的特定突触前速率进行调整（图 2B）。

===Temporal filtering===

==='''时间过滤'''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: Derivation of a temporal filter for short-term depression|Appendix A]] we present the corresponding calculation for depression-dominated synapses (<math>u^+ \approx U</math>). By considering small perturbations<math>R(t):=R_0 + R_1 \rho (t)</math>with <math>R_1\ll R_0</math>around the constant rate <math>R_0>0</math>, the Fourier transform of the synaptic current <math>I</math>is approximated by

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: Derivation of a temporal filter for short-term depression|Appendix A]] we present the corresponding calculation for depression-dominated synapses (<math>u^+ \approx U</math>). By considering small perturbations<math>R(t):=R_0 + R_1 \rho (t)</math>with <math>R_1\ll R_0</math>around the constant rate <math>R_0>0</math>, the Fourier transform of the synaptic current <math>I</math>is approximated by

上述分析仅描述了具有固定放电率的神经群体放电。当突触前群体放电率随时间任意变化时， 方程（3）可用于推导动态突触的过滤特性。 在附录 A 中，我们给出了抑郁支配突触的相应计算 (<math>u^+ \approx U</math>)。 通过考虑小扰动<math>R(t):=R_0 + R_1 \rho (t)</math>和<math>R_1\ll R_0</math>在恒定速率<math>R_0>0</math>附近，突触电流 <math>I</math>的傅里叶变换近似为

上述分析仅描述了具有固定放电率的神经群体放电。当突触前群体放电率随时间任意变化时， 方程（3）可用于推导动态突触的过滤特性。 在附录 A 中，我们给出了抑郁支配突触的相应计算 (<math>u^+ \approx U</math>)。 通过考虑小扰动<math>R(t):=R_0 + R_1 \rho (t)</math>和<math>R_1\ll R_0</math>在恒定速率<math>R_0>0</math>附近，突触电流 <math>I</math>的傅里叶变换近似为

STP can also regulate information transmission in other ways. For instance, STD may contribute to remove auto-correlation in temporal inputs, since temporally proximal spikes tend to magnify the depression effect and hence reduce the output correlation of the post-synaptic potential ([[#Goldman02|Goldman 02]]). On the other hand, STF, whose effect is enlarged by temporally proximal spikes, improves the sensitivity of a post-synaptic neuron to temporally correlated inputs ([[#Mejías08|Mejías 08]], [[#Bourjaily12|Bourjaily 12]]).

STP can also regulate information transmission in other ways. For instance, STD may contribute to remove auto-correlation in temporal inputs, since temporally proximal spikes tend to magnify the depression effect and hence reduce the output correlation of the post-synaptic potential ([[#Goldman02|Goldman 02]]). On the other hand, STF, whose effect is enlarged by temporally proximal spikes, improves the sensitivity of a post-synaptic neuron to temporally correlated inputs ([[#Mejías08|Mejías 08]], [[#Bourjaily12|Bourjaily 12]]).

STP 还可以通过其他方式规范信息传输。 例如，STD 可能有助于消除时间输入中的自相关，因为时间近端尖峰倾向于放大抑郁效应，从而降低突触后电位的输出相关性 (Goldman 02)。 另一方面，STF 的效果因时间近端尖峰而扩大，提高了突触后神经元对时间相关输入的敏感性 (Mejías 08, Bourjaily 12)。

STP 还可以通过其他方式规范信息传输。 例如，STD 可能有助于消除时间输入中的自相关，因为时间近端尖峰倾向于放大抑郁效应，从而降低突触后电位的输出相关性 ([[#Goldman02|Goldman 02]])。 另一方面，STF 的效果因时间近端尖峰而扩大，提高了突触后神经元对时间相关输入的敏感性([[#Mejías08|Mejías 08]], [[#Bourjaily12|Bourjaily 12]])。

By combining STD and STF, neural information transmission could be further improved. For example, by combining STF-dominated excitatory and STD-dominated inhibitory synapses, the detection of high-frequency epochs by a postsynaptic neuron can be enhanced ([[#Klyachko06|Klyachko 06]]). In a postsynaptic neuron receiving both STD-dominated and STF-dominated inputs, the neural response can show both low- and high-pass filtering properties ([[#Fortune01|Fortune 01]]).

By combining STD and STF, neural information transmission could be further improved. For example, by combining STF-dominated excitatory and STD-dominated inhibitory synapses, the detection of high-frequency epochs by a postsynaptic neuron can be enhanced ([[#Klyachko06|Klyachko 06]]). In a postsynaptic neuron receiving both STD-dominated and STF-dominated inputs, the neural response can show both low- and high-pass filtering properties ([[#Fortune01|Fortune 01]]).

通过结合STD和STF，可以进一步改善神经信息传输。 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期的检测 (Klyachko 06)。 在同时接收 STD 主导和 STF 主导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（[[#Fortune01|Fortune 01]]）。

通过结合STD和STF，可以进一步改善神经信息传输。 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期的检测([[#Klyachko06|Klyachko 06]])。 在同时接收 STD 主导和 STF 主导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（[[#Fortune01|Fortune 01]]）。

===Gain control===

===增益控制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 ([[#Abbott97|Abbott 97]], [[#Abbott04|Abbott 04]], [[#Cook03|Cook 03]]). If a steady presynaptic firing rate <math>R</math> changes abruptly by an amount <math>\Delta R</math>, the first spike at the new rate will be transmitted with the efficacy <math>E</math> before the synapse is further depressed. Thus, the transient increase in synaptic input will be proportional to <math>\Delta R E(R)</math>, which is approximately proportional to <math>\Delta R/R</math> 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 <math>\Delta R</math>, 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.

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 ([[#Abbott97|Abbott 97]], [[#Abbott04|Abbott 04]], [[#Cook03|Cook 03]]). If a steady presynaptic firing rate <math>R</math> changes abruptly by an amount <math>\Delta R</math>, the first spike at the new rate will be transmitted with the efficacy <math>E</math> before the synapse is further depressed. Thus, the transient increase in synaptic input will be proportional to <math>\Delta R E(R)</math>, which is approximately proportional to <math>\Delta R/R</math> 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 <math>\Delta R</math>, 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.

由于 STD 以频率依赖性方式抑制突触功效，因此有人提出 STD 提供了一种自动机制来实现增益控制，即<math>\Delta R</math>通过将高增益分配给缓慢放电的传入神经并将低增益分配给快速放电的传入神经（[[#Abbott97|Abbott 97]], [[#Abbott04|Abbott 04]], [[#Cook03|Cook 03]])。如果一个稳定的突触前放电率 <math>R</math>突然改变了<math>\Delta R</math>的量，那么新的突触前放电率将与在突触被进一步抑制之前的功效 <math>E</math>。因此，突触输入的瞬时增加将与 <math>\Delta R E(R)</math>成正比，这与<math>\Delta R/R</math>大致成正比] 对于大利率（见上文）。这让人想起韦伯定律，该定律指出瞬态突触反应大致与输入放电率的百分比变化成正比。图 2D 显示对于固定大小的速率变化<math>\Delta R</math>，响应随着稳定输入值的变化而减小；而在没有 STD 的情况下，对于固定大小的速率变化，响应将是恒定的。

由于 STD 以频率依赖性方式抑制突触功效，因此有人提出 STD 提供了一种自动机制来实现增益控制，即<math>\Delta R</math>通过将高增益分配给缓慢放电的传入神经并将低增益分配给快速放电的传入神经（[[#Abbott97|Abbott 97]], [[#Abbott04|Abbott 04]], [[#Cook03|Cook 03]])。如果一个稳定的突触前放电率 <math>R</math>突然改变了<math>\Delta R</math>的量，那么新的突触前放电率将与在突触被进一步抑制之前的功效 <math>E</math>。因此，突触输入的瞬时增加将与 <math>\Delta R E(R)</math>成正比，这与<math>\Delta R/R</math>大致成正比] 对于大利率（见上文）。这让人想起韦伯定律，该定律指出瞬态突触反应大致与输入放电率的百分比变化成正比。图 2D 显示对于固定大小的速率变化<math>\Delta R</math>，响应随着稳定输入值的变化而减小；而在没有 STD 的情况下，对于固定大小的速率变化，响应将是恒定的。