104
个编辑
更改
跳到导航
跳到搜索
第1行：
第1行： − <!DOCTYPE html> − <html lang="en" dir="ltr" class="client-nojs"> − <head> − <title>Short-term synaptic plasticity - Scholarpedia</title> − <meta charset="UTF-8" /> − <meta name="generator" content="MediaWiki 1.19.17" /> − <meta name="citation_title" content="Short-term synaptic plasticity" /> − <meta name="citation_author" content="Misha Tsodyks" /> − <meta name="citation_author" content="Si Wu" /> − <meta name="citation_date" content="2013/10/11" /> − <meta name="citation_journal_title" content="Scholarpedia" /> − <meta name="citation_issn" content="1941-6016" /> − <meta name="citation_volume" content="8" /> − <meta name="citation_issue" content="10" /> − <meta name="citation_firstpage" content="3153" /> − <meta name="citation_doi" content="10.4249/scholarpedia.3153" /> − <link rel="shortcut icon" href="/w/images/6/64/Favicon.ico" /> − <link rel="search" type="application/opensearchdescription+xml" href="/w/opensearch_desc.php" title="Scholarpedia (en)" /> − <link rel="EditURI" type="application/rsd+xml" href="http://www.scholarpedia.org/w/api.php?action=rsd" /> − <link rel="alternate" type="application/atom+xml" title="Scholarpedia Atom feed" href="/w/index.php?title=Special:RecentChanges&amp;feed=atom" /> − <link rel="stylesheet" href="http://www.scholarpedia.org/w/load.php?debug=false&amp;lang=en&amp;modules=mediawiki.legacy.commonPrint%2Cshared%7Cskins.vector&amp;only=styles&amp;skin=vector&amp;*" /> − <link rel="stylesheet" href="/w/skins/vector/font-awesome.min.css" /> − <link rel="stylesheet" href="/w/skins/vector/local-screen.css" /><meta name="ResourceLoaderDynamicStyles" content="" /> − <link rel="stylesheet" href="http://www.scholarpedia.org/w/load.php?debug=false&amp;lang=en&amp;modules=site&amp;only=styles&amp;skin=vector&amp;*" /> − <style>a:lang(ar),a:lang(ckb),a:lang(fa),a:lang(kk-arab),a:lang(mzn),a:lang(ps),a:lang(ur){text-decoration:none}a.new,#quickbar a.new{color:#ba0000} − − /* cache key: wikidb:resourceloader:filter:minify-css:7:c88e2bcd56513749bec09a7e29cb3ffa */</style> − − <nowiki><script src="http://www.scholarpedia.org/w/load.php?debug=false&amp;lang=en&amp;modules=startup&amp;only=scripts&amp;skin=vector&amp;*"></script></nowiki> − <nowiki><script>if(window.mw){ − mw.config.set({"wgCanonicalNamespace":"","wgCanonicalSpecialPageName":false,"wgNamespaceNumber":0,"wgPageName":"Short-term_synaptic_plasticity","wgTitle":"Short-term synaptic plasticity","wgCurRevisionId":182521,"wgArticleId":3153,"wgIsArticle":true,"wgAction":"view","wgUserName":null,"wgUserGroups":["*"],"wgCategories":["Neuroscience"],"wgBreakFrames":false,"wgPageContentLanguage":"en","wgSeparatorTransformTable":["",""],"wgDigitTransformTable":["",""],"wgRelevantPageName":"Short-term_synaptic_plasticity","wgRestrictionEdit":[],"wgRestrictionMove":[],"wgVectorEnabledModules":{"collapsiblenav":true,"collapsibletabs":true,"editwarning":false,"expandablesearch":false,"footercleanup":false,"sectioneditlinks":false,"simplesearch":true,"experiments":true}}); − }</script></nowiki><nowiki><script>if(window.mw){ − mw.loader.implement("user.options",function($){mw.user.options.set({"ccmeonemails":0,"cols":80,"date":"default","diffonly":0,"disablemail":0,"disablesuggest":0,"editfont":"default","editondblclick":0,"editsection":1,"editsectiononrightclick":0,"enotifminoredits":0,"enotifrevealaddr":0,"enotifusertalkpages":1,"enotifwatchlistpages":1,"extendwatchlist":0,"externaldiff":0,"externaleditor":0,"fancysig":0,"forceeditsummary":0,"gender":"unknown","hideminor":0,"hidepatrolled":0,"highlightbroken":1,"imagesize":2,"justify":0,"math":1,"minordefault":0,"newpageshidepatrolled":0,"nocache":0,"noconvertlink":0,"norollbackdiff":0,"numberheadings":0,"previewonfirst":0,"previewontop":1,"quickbar":5,"rcdays":7,"rclimit":50,"rememberpassword":0,"rows":25,"searchlimit":20,"showhiddencats":0,"showjumplinks":1,"shownumberswatching":1,"showtoc":1,"showtoolbar":1,"skin":"vector","stubthreshold":0,"thumbsize":2,"underline":2,"uselivepreview":0,"usenewrc":0,"watchcreations":0,"watchdefault":0,"watchdeletion":0, − "watchlistdays":3,"watchlisthideanons":0,"watchlisthidebots":0,"watchlisthideliu":0,"watchlisthideminor":0,"watchlisthideown":0,"watchlisthidepatrolled":0,"watchmoves":0,"wllimit":250,"vector-simplesearch":1,"vector-collapsiblenav":1,"variant":"en","language":"en","searchNs0":true,"searchNs1":false,"searchNs2":false,"searchNs3":false,"searchNs4":false,"searchNs5":false,"searchNs6":false,"searchNs7":false,"searchNs8":false,"searchNs9":false,"searchNs10":false,"searchNs11":false,"searchNs12":false,"searchNs13":false,"searchNs14":false,"searchNs15":false,"searchNs200":false,"searchNs201":false,"searchNs400":false,"searchNs401":false});;},{},{});mw.loader.implement("user.tokens",function($){mw.user.tokens.set({"editToken":"+\\","watchToken":false});;},{},{});</nowiki> − − /* cache key: wikidb:resourceloader:filter:minify-js:7:e87579b4b142a5fce16144e6d8ce1889 */ − }</script> − <script>if(window.mw){ − mw.loader.load(["mediawiki.page.startup","mediawiki.legacy.wikibits","mediawiki.legacy.ajax"]); − }</script> − <link rel="canonical" href="http://www.scholarpedia.org/article/Short-term_synaptic_plasticity" /> − <!--[if lt IE 7]><style type="text/css">body{behavior:url("/w/skins/vector/csshover.min.htc")}</style><![endif]--></head> − <body class="mediawiki ltr sitedir-ltr ns-0 ns-subject page-Short-term_synaptic_plasticity skin-vector action-view cp-body-published"> − <div class="noprint"></div> − <div class="noprint"></div> − <!-- content --> − <div id="content" class="mw-body"> − <a id="top"></a> − <div style="display:none;"></div> − <!-- sitenotice --> − <div id="siteNotice"><script type="text/javascript"> − /* <![CDATA[ */ − document.writeln("\x3cdiv id=\"localNotice\" lang=\"en\" dir=\"ltr\"\x3e\x3cp style=text-align:left;font-style:italic\x3eScholarpedia is supported by \x3ca href=\'http://www.braincorp.com\'\x3eBrain Corporation\x3c/a\x3e\x3c/p\x3e\x3c/div\x3e"); − /* ]]> */ − − </script></div> − <!-- /sitenotice --> − <!-- firstHeading --> − <h1 id="firstHeading" class="firstHeading"> − <span dir="auto">Short-term synaptic plasticity</span> − </h1> − <!-- /firstHeading --> − − <div class="cp-googleplus"> − <div class="g-plusone" align="right" data-size="small" data-annotation="inline" data-width="180"></div> − <script type="text/javascript"> − (function () { − var po = document.createElement('script'); − po.type = 'text/javascript'; − po.async = true; − po.src = 'https://apis.google.com/js/plusone.js'; − var s = document.getElementsByTagName('script')[0]; − s.parentNode.insertBefore(po, s); − })(); − </script> − </div> − − <!-- bodyContent --> − <div id="bodyContent"> − <!-- tagline --> − <div id="siteSub">From Scholarpedia</div> − <!-- /tagline --> − <!-- subtitle --> − <div id="contentSub"><span class="subpages"><table class="cp-citation-subtitle" width="100%" cellpadding="0" cellspacing="0" border="0"> − <tr valign="bottom"> − <td align="left">Misha Tsodyks and Si Wu (2013), Scholarpedia, 8(10):3153.</td> − <td align="center"><a href="http://dx.doi.org/10.4249/scholarpedia.3153">doi:10.4249/scholarpedia.3153</a></td> − <td align="right">revision #182521 [<nowiki><a href="/w/index.php?title=Short-term_synaptic_plasticity&amp;action=cite&amp;rev=182521" title="Short-term synaptic plasticity">link to/cite this article</a></nowiki>]</td> − </tr> − </table> − </span></div> − <!-- /subtitle --> − <!-- jumpto --> − <div id="jump-to-nav" class="mw-jump"> − Jump to: <a href="#mw-head">navigation</a>, − <a href="#p-search">search</a> − </div> − <!-- /jumpto --> − <!-- bodycontent --> − <div lang="en" dir="ltr" class="mw-content-ltr"><div class="cp-box-container"><div class="cp-curator-box noprint"><b><u>Post-publication activity</u></b><br /><button class="cp-button btn"></button><p><span class="cp-title-label">Curator:</span> <a href="/article/User:Si_Wu" title="User:Si Wu">Si Wu</a> − </p><div class="cp-assistants hidden"><div><span class="cp-title-label">Contributors:</span><p> </p></div><div><span>0.67 - </span><p><a href="/article/User:Tiziano_D%27Albis" title="User:Tiziano D'Albis">Tiziano D'Albis</a> − </p></div><div><span>0.33 - </span><p><a href="/article/User:Misha_Tsodyks" title="User:Misha Tsodyks">Misha Tsodyks</a> − </p></div><div><span></span><p><a href="/article/User:Stefano_Fusi" title="User:Stefano Fusi">Stefano Fusi</a> − </p></div><div><span></span><p><a href="/article/User:Boris_Gutkin" title="User:Boris Gutkin">Boris Gutkin</a> − </p></div><div><span></span><p><a href="/article/User:Eugene_M._Izhikevich" title="User:Eugene M. Izhikevich">Eugene M. Izhikevich</a> − </p></div></div></div></div><div class="cp-author-order"><ul id="sp_authors"><li id="sort-1"><p><a href="/article/User:Misha_Tsodyks" title="User:Misha Tsodyks"><span class="bold">Dr. Misha Tsodyks</span>, Weizmann Institute, Rehovot, Israel</a> − </p></li><li id="sort-2"><p><a href="/article/User:Si_Wu" title="User:Si Wu"><span class="bold">Prof. Si Wu</span>, State Key Lab of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China</a> − </p></li></ul></div><p>Short-term plasticity (STP) (<a href="#Stevens95">Stevens 95</a>, <a href="#Markram96">Markram 96</a>, <a href="#Abbott97">Abbott 97</a>, <a href="#Zucker02">Zucker 02</a>, <a href="#Abbott04">Abbott 04</a>), 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 (<a href="#Markram98">Markram 98</a>, <a href="#Dittman00">Dittman 00</a>, <a href="#Wang06">Wang 06</a>). Synapses in different cortical areas can have varied forms of plasticity, being either STD-dominated, STF-dominated, or showing a mixture of both forms. − </p><p>Compared with long-term plasticity (<a href="#Bi01">Bi 01</a>), which is hypothesized as the <a href="/article/Neuron" title="Neuron">neural</a> 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. − </p><p>Although STP appears to be an unavoidable consequence of synaptic physiology, theoretical studies suggest that its role in <a href="/article/Brain" title="Brain">brain</a> functions can be profound (see, e.g., publications in (<a href="#ResearchTopic">Research Topic</a>) 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 <a href="/article/Working_memory" title="Working memory">working memory</a>. 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 <a href="/article/Encyclopedia_of_Computational_Neuroscience" title="Encyclopedia of Computational Neuroscience">Computational Neuroscience</a>. − </p> − <table class="toc" id="toc"><tr><td><div id="toctitle"><h2>Contents</h2></div> − <ul> − <li class="toclevel-1 tocsection-1"><a href="#Phenomenological_model"><span class="tocnumber">1</span> <span class="toctext">Phenomenological model</span></a></li> − <li class="toclevel-1 tocsection-2"><a href="#Effects_on_information_transmission"><span class="tocnumber">2</span> <span class="toctext">Effects on information transmission</span></a> − <ul> − <li class="toclevel-2 tocsection-3"><a href="#Temporal_filtering"><span class="tocnumber">2.1</span> <span class="toctext">Temporal filtering</span></a></li> − <li class="toclevel-2 tocsection-4"><a href="#Gain_control"><span class="tocnumber">2.2</span> <span class="toctext">Gain control</span></a></li> − </ul> − </li> − <li class="toclevel-1 tocsection-5"><a href="#Effects_on_network_dynamics"><span class="tocnumber">3</span> <span class="toctext">Effects on network dynamics</span></a> − <ul> − <li class="toclevel-2 tocsection-6"><a href="#Prolongation_of_neural_responses_to_transient_inputs"><span class="tocnumber">3.1</span> <span class="toctext">Prolongation of neural responses to transient inputs</span></a></li> − <li class="toclevel-2 tocsection-7"><a href="#Modulation_of_network_responses_to_external_input"><span class="tocnumber">3.2</span> <span class="toctext">Modulation of network responses to external input</span></a></li> − <li class="toclevel-2 tocsection-8"><a href="#Induction_of_instability_or_mobility_of_network_state"><span class="tocnumber">3.3</span> <span class="toctext">Induction of instability or mobility of network state</span></a></li> − <li class="toclevel-2 tocsection-9"><a href="#Enrichment_of_attractor_dynamics"><span class="tocnumber">3.4</span> <span class="toctext">Enrichment of attractor dynamics</span></a></li> − </ul> − </li> − <li class="toclevel-1 tocsection-10"><a href="#Appendix_A:_Derivation_of_a_temporal_filter_for_short-term_depression"><span class="tocnumber">4</span> <span class="toctext">Appendix A: Derivation of a temporal filter for short-term depression</span></a></li> − <li class="toclevel-1 tocsection-11"><a href="#References"><span class="tocnumber">5</span> <span class="toctext">References</span></a></li> − </ul> − </td></tr></table> − <h2> <span class="mw-headline" id="Phenomenological_model"> Phenomenological model </span></h2> − <p>The biophysical processes underlying STP are complex. Studies of the computational roles of STP have relied on the creation of simplified phenomenological models (<a href="#Abbott97">Abbott 97</a>,<a href="#Markram98">Markram 98</a>,<a href="#Tsodyks98">Tsodyks 98</a>). − </p><p>In the model proposed by Tsodyks and Markram (<a href="#Tsodyks98">Tsodyks 98</a>), the STD effect is modeled by a normalized variable \(x\) (\(0\leq x \leq1\)), denoting the fraction of resources that remain available after neurotransmitter depletion. The STF effect is modeled by a utilization parameter \(u\), representing the fraction of available resources ready for use (release probability). Following a spike, (i) \(u\) increases due to spike-induced calcium influx to the presynaptic terminal, after which (ii) a fraction \(u\) of available resources is consumed to produce the post-synaptic current. Between spikes, \(u\) decays back to zero with time constant \(\tau_f\) and \(x\) recovers to 1 with time constant \(\tau_d \). In summary, the <a href="/article/Dynamical_Systems" title="Dynamical Systems">dynamics</a> of STP is given by − </p><p><span id="Eq-1">\[\begin{aligned} − \frac{du}{dt} & = & -\frac{u}{\tau_f}+U(1-u^-)\delta(t-t_{sp}),\nonumber \\ \frac{dx}{dt} & = & \frac{1-x}{\tau_d}-u^+x^-\delta(t-t_{sp}), \\ − \frac{dI}{dt} & = & -\frac{I}{\tau_s} + Au^+x^-\delta(t-t_{sp}), \nonumber − \tag{1}\end{aligned}\] − </p><p>where \(t_{sp}\) denotes the spike time and \(U\) is the increment of \(u\) produced by a spike. We denote as \(u^-, x^-\) the corresponding variables just before the arrival of the spike, and \(u^+\) refers to the moment just after the spike. From the first equation, \(u^+ = u^- + U(1-u^-)\). The synaptic current generated at the synapse by the spike arriving at \(t_{sp}\) is then given by − </p><p><span id="Eq-2">\[\Delta I(t_{sp}) = Au^+x^-, − \tag{2}\] − </p><p>where \(A\) denotes the response amplitude that would be produced by total release of all the neurotransmitter (\(u=x=1\)), called absolute synaptic efficacy of the connections (see Fig. 1A). − </p><p>The interplay between the dynamics of \(u\) and \(x\) determines whether the joint effect of \(ux\) is dominated by depression or facilitation. In the parameter regime of \(\tau_d\gg \tau_f\) and large \(U\), an initial spike incurs a large drop in \(x\) that takes a long time to recover; therefore the synapse is STD-dominated (Fig.1B). In the regime of \(\tau_f \gg \tau_d\) and small \(U\), the synaptic efficacy is increased gradually by spikes, and consequently the synapse is STF-dominated (Fig.1C). This phenomenological model successfully reproduces the kinetic dynamics of depressed and facilitated synapses observed in many cortical areas. − </p><p><a href="/article/File:Fig1A_short_term_plasticity.png" class="image"><img alt="Fig1A short term plasticity.png" src="/w/images/thumb/4/4a/Fig1A_short_term_plasticity.png/400px-Fig1A_short_term_plasticity.png" width="400" height="300" /></a> − <a href="/article/File:Fig1B_short_term_plasticity.png" class="image"><img alt="Fig1B short term plasticity.png" src="/w/images/thumb/7/73/Fig1B_short_term_plasticity.png/350px-Fig1B_short_term_plasticity.png" width="350" height="263" /></a> − <a href="/article/File:Fig1C_short_term_plasticity.png" class="image"><img alt="Fig1C short term plasticity.png" src="/w/images/thumb/9/95/Fig1C_short_term_plasticity.png/350px-Fig1C_short_term_plasticity.png" width="350" height="263" /></a> <br /> − Figure 1. (A) The phenomenological model for STP given by Eqs.(<a href="#Eq-1">1</a>) and (<a href="#Eq-2">2</a>). (B) The post-synaptic current generated by an STD-dominated synapse. The neuronal firing rate \(R=15\)Hz. The parameters \(A=1\), \(U=0.45\), \(\tau_s=20\)ms, \(\tau_d=750\)ms, and \(\tau_f=50\)ms. (C) The dynamics of a STF-dominating synapse. The parameters \(U=0.15\), \(\tau_f=750\)ms, and \(\tau_d=50\)ms. − </p> − <h2> <span class="mw-headline" id="Effects_on_information_transmission"> Effects on information transmission </span></h2> − <p>Because STP modifies synaptic efficacy based on the history of presynaptic activity, it can alter neural information transmission (<a href="#Abbott97">Abbott 97</a>, <a href="#Tsodyks97">Tsodyks 97</a>, <a href="#Fuhrmann02">Fuhrmann 02</a>, <a href="#Rotman11">Rotman 11</a>, <a href="#Rosenbaum12">Rosenbaum 12</a>). 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 <a href="/article/Bursting" title="Bursting">bursts</a>, which increase the synaptic strength. − </p><p>Firing-rate-dependent transmission via dynamic synapses can be analyzed by examining the transmission of uncorrelated Poisson <a href="/article/Spike_train_and_point_processes" title="Spike train and point processes">spike trains</a> from a large neuronal population with global firing rate \(R(t)\). The time evolution for the postsynaptic current \(I(t)\) can be obtained by <a href="/article/Averaging" title="Averaging">averaging</a> Eq. (<a href="#Eq-1">1</a>) over different realization of Poisson processes corresponding to different spike trains (<a href="#Tsodyks98">Tsodyks 98</a>): − </p><p><span id="Eq-3">\[\begin{aligned} − \frac{du}{dt} & = & -\frac{u}{\tau_f} + U(1-u^-)R(t),\nonumber \\ − \frac{dx}{dt} & = & \frac{1-x}{\tau_d}-u^+xR(t), \\ − I(t) &= & \tau_s Au^+xR(t), \nonumber − \tag{3}\end{aligned} \] − </p><p>where again \(u^+ = u^- + U(1-u^-)\) and we neglect time scales on the order of the synaptic time constant. For the stationary rate, \(R(t) \equiv R_0\), we obtain − </p><p><span id="Eq-4">\[\begin{aligned} − u^+=u_0 & \equiv & U\frac{1+\tau_fR_0}{1+U\tau_fR_0}, \nonumber \\ − x=x_0 & \equiv & \frac{1}{1+u_0\tau_d R_0},\\ − I=I_0 & \equiv & \tau_s Au_0x_0 R_0, \nonumber \tag{4} \end{aligned}\] − </p><p>which is shown in Fig. 2A,B. In particular, for depression-dominated synapses (\(u^+ \approx U\)), the average synaptic efficacy \(E=Au^+x\) decays inversely with the rate, and the stationary synaptic current saturates at the limiting frequency \(\lambda \sim \frac{1}{U\tau_d}\), 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). − </p> − <h3> <span class="mw-headline" id="Temporal_filtering"> Temporal filtering </span></h3> − <p>The above analysis only describes neural population firing with stationary firing rates. Eq. (<a href="#Eq-3">3</a>) can be used to derive the filtering properties of dynamic synapses when the presynaptic population firing rate changes arbitrarily with time. In <a href="#Appendix_A:_Derivation_of_a_temporal_filter_for_short-term_depression">Appendix A</a> we present the corresponding calculation for depression-dominated synapses (\(u^+ \approx U\)). By considering small perturbations $R(t):=R_0 + R_1 \rho (t)$ with $R_1\ll R_0$ around the constant rate $R_0>0 $, the Fourier transform of the synaptic current $I$ is approximated by − </p><p><span id="Eq-5">\( − \begin{eqnarray} − \widehat{I}(\omega) \approx I_0 \delta(\omega) + \frac{I_0 R_1}{R_0} \widehat{\chi}(\omega) \widehat{\rho}(\omega) − \tag{5} − \end{eqnarray} − \) − where we defined the filter − <span id="Eq-6">\( − \begin{eqnarray} − \widehat{\chi}(\omega) <nowiki>:= 1- \frac{1/x_0 -1}{1/x_0 + j\omega \tau_{d}} = \frac{1+(\tau_{d}\omega)^2x_0+j\omega\tau_{d}(1-x_0)}{1/x_0+(\tau_{d}\omega)^2 x_0}\,, − \tag{6} − \end{eqnarray} − \)</nowiki></span> − </p><p>$\widehat{\rho}$ is the Fourier transform of $\rho$, and $I_0$ and $x_0$ are the stationary values of $I$ and $x$, respectively [see Eq. (<nowiki><a href="#Eq-4">4</a></nowiki>) with $u_0 = U$]. − The amplitude of the filter \(|\widehat{\chi}(w)|\) is shown in Fig. 2C, illustrating the high-pass filter properties of depressing synapses. In other words, fast changes in presynaptic firing rates are faithfully transmitted to the postsynaptic targets, while slow changes are attenuated by depression. − </p><p>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 (<a href="#Goldman02">Goldman 02</a>). 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 (<a href="#Mej.C3.ADas08">Mejías 08</a>, <a href="#Bourjaily12">Bourjaily 12</a>). − </p><p>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 (<a href="#Klyachko06">Klyachko 06</a>). In a postsynaptic neuron receiving both STD-dominated and STF-dominated inputs, the neural response can show both low- and high-pass filtering properties (<a href="#Fortune01">Fortune 01</a>). − </p> − <h3> <span class="mw-headline" id="Gain_control"> Gain control </span></h3> − <p>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 (<a href="#Abbott97">Abbott 97</a>, <a href="#Abbott04">Abbott 04</a>, <a href="#Cook03">Cook 03</a>). If a steady presynaptic firing rate \(R\) changes abruptly by an amount \(\Delta R\), the first spike at the new rate will be transmitted with the efficacy \(E\) before the synapse is further depressed. Thus, the transient increase in synaptic input will be proportional to \(\Delta R E(R)\), which is approximately proportional to \(\Delta R/R\) 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 \(\Delta R\), 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. − </p><p><a href="/article/File:Fig2A_short_term_plasticity.png" class="image"><img alt="Fig2A short term plasticity.png" src="/w/images/thumb/2/28/Fig2A_short_term_plasticity.png/300px-Fig2A_short_term_plasticity.png" width="300" height="226" /></a> − <a href="/article/File:Fig2B_short_term_plasticity.png" class="image"><img alt="Fig2B short term plasticity.png" src="/w/images/thumb/6/65/Fig2B_short_term_plasticity.png/300px-Fig2B_short_term_plasticity.png" width="300" height="226" /></a> − <a href="/article/File:Fig2C_short_term_plasticity.png" class="image"><img alt="Fig2C short term plasticity.png" src="/w/images/thumb/2/25/Fig2C_short_term_plasticity.png/300px-Fig2C_short_term_plasticity.png" width="300" height="226" /></a> − <a href="/article/File:Fig2D_short_term_plasticity.png" class="image"><img alt="Fig2D short term plasticity.png" src="/w/images/thumb/6/6c/Fig2D_short_term_plasticity.png/300px-Fig2D_short_term_plasticity.png" width="300" height="226" /></a> <br /> − Figure 2. (A) The steady values of the efficacy of an STD-dominated synapse and the postsynaptic currents it generates, measured by \(ux\) and \(uxR\), respectively. The parameters are the same as in Fig.1B. (B) Same as (A) for an STF-dominated synapse. The parameters are the same as in Fig. 1C. (C) The filtering properties of an STD-dominated synapse, measured by \(|\widehat{\chi}(w)|\) [Eq. (<nowiki><a href="#Eq-6">6</a></nowiki>)]. (D) The neural response to an abrupt input change \(\Delta R\) vs. the steady rate value for a STD-dominating synapse. \(\Delta R=5\)Hz. The parameters are the same as in Fig.1B. − </p> − <h2> <span class="mw-headline" id="Effects_on_network_dynamics"> Effects on network dynamics </span></h2> − <p>In addition to feedforward and feedback transmission, neural circuits generate recurrent interactions between neurons. With STP included in the recurrent interactions, the network dynamics exhibits many new interesting behaviors that do not arise with purely static synapses. These new dynamical properties could therefore implement STP-mediated network computation. − </p> − <h3> <span class="mw-headline" id="Prolongation_of_neural_responses_to_transient_inputs"> Prolongation of neural responses to transient inputs </span></h3> − <p>Since STP has a much longer time scale than that of single neuron dynamics (the latter is typically in the time order of \(10-20\) 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 <a href="/article/Memory" title="Memory">memory</a> 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 (<a href="#Karmorkar07">Karmorkar 07</a>). 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 (<a href="#Buonomano09">Buonomano 09</a>). 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 (<a href="#Mongillo08">Mongillo 08</a>). − </p> − <h3> <span class="mw-headline" id="Modulation_of_network_responses_to_external_input"> Modulation of network responses to external input </span></h3> − <p>Since STP modifies synaptic efficacy instantly, it can modulate the network response to sustained external inputs. An example of this is bursty <a href="/article/Synchronization" title="Synchronization">synchronous</a> firing in an STD-dominated network, either spontaneously or in response to external inputs. The resulting bursts of activity are called population spikes (<a href="#Loebel02">Loebel 02</a>). 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 (<a href="#Barak07">Barak 07</a>). The varied response properties mediated by STP may provide different ways of representing and conveying the stimulus information in a network. − </p> − <h3> <span class="mw-headline" id="Induction_of_instability_or_mobility_of_network_state"> Induction of <a href="/article/Stability" title="Stability">instability</a> or mobility of network state </span></h3> − <p>Persistent firing, referring to situations in which a group of neurons continue firing without external drive, is widely regarded as a neural substrate for information representation (<a href="#Fuster71">Fuster 71</a>). To maintain persistent activity in a network, strong excitatory recurrent interactions between neurons are needed to establish a positive-feedback loop sustaining neuronal responses. Mathematically, persistent activity is often modeled as an active stationary state (<a href="/article/Attractor" title="Attractor">attractor</a>) of the network. Since STD weakens synaptic efficacy depending on the level of neuronal activity, it can suppress an attractor state. This property, however, can be used to carry out valuable computations. − </p><p>Consider a network that holds multiple attractor states competing with each other, STD destabilizing one of them can incur the network to switch to another attractor state (<a href="#Torres07">Torres 07</a>, <a href="#Katori11">Katori 11</a>, <a href="#Igarashi12">Igarashi 12</a>). This property has been linked to spontaneous transition between up and down states of cortical neurons (<a href="#Holcman06">Holcman 06</a>), to the binocular rivalry phenomenon (<a href="#Kilpatrick10">Kilpatrick 10</a>), and to enhanced discrimination capacity for superimposed ambiguous inputs (<a href="#Fung13">Fung 13</a>). STF can also induce state switching, but this is achieved in an indirect way through facilitating the excitatory synapses to interneurons, with the latter in turn suppressing excitatory neurons (<a href="#Melamed08">Melamed 08</a>). − </p><p>The joint effect of STD and STF on the memory capacity of the classical <a href="/article/Hopfield_Network" title="Hopfield Network">Hopfield model</a> has been investigated (<a href="#Mej.C3.ADas09">Mejías 09</a>). It was found that STD degrades the memory capacity of the network, but induces a novel computationally desirable property, that is, the network can hop among memory states, which could be useful for memory searching. Interestingly, STF can compensate for the lost memory capacity caused by STD. − </p> − <h3> <span class="mw-headline" id="Enrichment_of_attractor_dynamics"> Enrichment of attractor dynamics </span></h3> − <p>Continuous Attractor Neural Networks (CANNs), also called neural field models or ring models (<a href="#Amari77">Amari 77</a>), have been widely used to describe the encoding of continuous stimuli in the neural system, such as for head-direction, orientation, movement direction, and spatial location of objects. A CANN, due to its translation-invariant recurrent interactions between neurons, holds a continuous family of localized stationary states, called bumps. These stationary states form a subspace on which the network is neutrally stable, enabling the network to track time-varying stimuli smoothly. − </p><p>With STP included, a CANN displays new interesting dynamical behaviors. One of them is a spontaneous <a href="/article/Traveling_Waves" title="Traveling Waves">traveling wave</a> phenomenon (<a href="#York09">York 09</a>, <a href="#Fung12a">Fung 12</a>, <a href="#Bressloff12">Bressloff 12</a>) (Fig.3C). Consider a network that is initially in a localized bump state. Because of STD, the neural interactions in the bump region are weakened. As a result of competition from neighboring attractor states, a small displacement will push the bump away, and it will continue to move in that direction due to the STD effect. If the network is driven by a continuously moving input, in a proper parameter regime the bump movement can even lead the external drive by a constant time irrespective to the input moving speed, achieving an anticipative behavior that is reminiscent to the predictive responses of head-direction neurons in rodents (Fig.3D; <a href="#Fung12b">Fung 12</a>). − </p><p><a href="/article/File:Fig3AB_short_term_plasticity.png" class="image"><img alt="Fig3AB short term plasticity.png" src="/w/images/thumb/e/e2/Fig3AB_short_term_plasticity.png/700px-Fig3AB_short_term_plasticity.png" width="700" height="248" /></a> − <a href="/article/File:Fig3C-TravellingWave.gif" class="image"><img alt="Fig3C-TravellingWave.gif" src="/w/images/0/02/Fig3C-TravellingWave.gif" width="336" height="252" /></a> − <a href="/article/File:Fig3D-Leading.gif" class="image"><img alt="Fig3D-Leading.gif" src="/w/images/c/c4/Fig3D-Leading.gif" width="336" height="252" /></a> <br /> − Figure 3. (A,B) Population spikes generated by a STD-dominating network in response to external excitatory pulses. When the presentation rate of the pulses is low (A), the network responds to each one of them. For higher presentation rate (B), the network only responds to a fraction of the inputs. Adapted from (<a href="#Loebel02">Loebel 02</a>). (C) The traveling wave generated by STD in a CANN. (D) The anticipative tracking behavior of a CANN with STD. − </p><p><br /> − </p> − <h2> <span class="mw-headline" id="Appendix_A:_Derivation_of_a_temporal_filter_for_short-term_depression"> Appendix A: Derivation of a temporal filter for short-term depression </span></h2> − <p>We consider the rate-based dynamics in Eq. (<a href="#Eq-3">3</a>) for depression-dominated synapses (\(u^+ \approx U\)) and for synaptic responses that are much faster than the depression dynamics ($\tau_s \ll \tau_d$)<span id="Eq-8">\[ − \begin{eqnarray} − {\frac{{\rm d} x}<nowiki>{{\rm d}t}}</nowiki>&=&<nowiki>\frac{1-x}{\tau_{d}}-Ux R(t) \tag{7}\\ − I(t) </nowiki>&= & \tau_{s} AU x R(t) \tag{8} \,. − \end{eqnarray} − \]</span> − </p><p>The aim is to derive a filter $\chi$ that relates the output synaptic current $I$ to the input rate $R$. − Note that because the input rate $R$ enters the equations in a multiplicative fashion the input-output transfer function is non linear. Yet a linear filter can be derived by considering small perturbations $R_1 \rho(t)$ of the firing rate $R(t)$ around a constant rate $R_0$, that is, − <span id="Eq-9">\( − R(t):=R_0 + R_1 \rho (t)\, \quad\text{with}\quad R_0,R_1>0 \quad\text{and}\quad R_1\ll R_0 \, . − \tag{9} − \)</span> − </p><p>We assume that such small perturbations in $R$ produce small perturbations in the variable $x$ around its steady state value $x_0>0$ <span id="Eq-10">\[ − <nowiki>x(t) = x_0 + x_1(t)\quad\text{with}\quad x_0 = \frac{1}{1+UR_0\tau_{d}} \quad\text{and}\quad |x_1(t)| \ll x_0 \, . − \tag{10} − \]</nowiki> − </p><p>We can now linearize the dynamics of $x(t)$ around the steady-state value $x_0$ by approximating the product − </p><p><span id="Eq-11">\( − \begin{eqnarray} − xR &=& (x_0+x_1)(R_0+R_1\rho)\\ − &=& x_0 R_0 + x_0 R_1 \rho + x_1 R_0+ x_1 R_1\rho\\ − &\approx& x_0 R_0 + x_0 R_1 \rho + x_1 R_0\\ − &\approx& R_0 x+ x_0R -x_0 R_0 \tag{11} − \end{eqnarray} − \) − </p><p>where in Eq. (<a href="#Eq-11">11</a>) we dropped the second-order term $x_1 R_1\rho$ because we assumed $R_1\ll R_0$ and $|x_1|\ll x_0$. Plugging Eq. (<a href="#Eq-11">11</a>) into Eq. (<a href="#Eq-7">7</a>) yields − </p><p><span id="Eq-12">\( − \begin{eqnarray} − {\frac<nowiki>{{\rm d} x}{{\rm d}t}} = \frac{1-x}{\tau_{d}}</nowiki> - U R_0 x - U x_0 R + U x_0 R_0\,.\tag{12} − \end{eqnarray} − \) − </p><p><br /> − We now take the Fourier transform at both sides of Eq. (<a href="#Eq-12">12</a>) − <span id="Eq-13">\( − \begin{eqnarray} − j\omega \tau_{d} \widehat{x} = -\widehat{x} - U R_0 \tau_{d} \widehat{x} - U x_0 \tau_{d}\widehat{R} + (1+ U R_0 \tau_{d} x_0) \delta(\omega) − \tag{13} − \end{eqnarray} − \)</span> − where we defined the Fourier transform pair − <span id="Eq-14">\( − \begin{eqnarray} − \widehat{x}(\omega) := \int \!{\rm d}{t}\, x(t) \exp(-j\omega t ) \quad ; \quad x(t) = \frac{1}{2\pi}\int \!{\rm d}\omega\, \widehat{x}(\omega) \exp(j\omega t) − \tag{14} − \end{eqnarray} − \)</span> − and $j=\sqrt{-1}$ is the imaginary unit. Solving Eq. (<a href="#Eq-13">13</a>) for the variable $\widehat{x}$, we find − <span id="Eq-15"><nowiki>\( − \begin{eqnarray} − \widehat{x} = -\frac{U\tau_{d}x_0}{1/x_0 + j \omega \tau_{d}} \widehat{R} + x_0 (2-x_0) \delta(\omega) \tag{15} − \end{eqnarray} − \)</nowiki></span> − where from Eq. (<a href="#Eq-10">10</a>) we used $U R_0 \tau_{d}=1/x_0 - 1$. − </p><p>Next, we plug Eq. (<a href="#Eq-11">11</a>) into Eq. (<a href="#Eq-8">8</a>) to linearize the dynamics of the synaptic current − </p><p><span id="Eq-16">\( − \begin{eqnarray} − I &=& \tau_{s}AU (R_0x+x_0R-x_0R_0)\\ − &=& I_0 \left( \frac{x}{x_0}+ \frac{R}{R_0}-1\right) \tag{16} − \end{eqnarray} − \) − around the steady-state value $I_0 = \tau_{s}AU x_0 R_0$. − </p><p>By taking the Fourier transform at both sides of Eq. (<a href="#Eq-16">16</a>), using Eq. (<a href="#Eq-15">15</a>), we obtain − <span id="Eq-17">\( − \begin{eqnarray} − \widehat{I} &=&<nowiki> I_0 \frac{\widehat{x}}{x_0} + I_0 \frac{\widehat{R}}{R_0} - I_0 \delta(\omega) \\</nowiki> − &=& \frac{I_0}{R_0} \widehat{\chi} \widehat{R} + I_0(1-x_0) \delta(\omega) − \tag{17} − \end{eqnarray} − \) − where we defined the filter − <span id="Eq-18">\( − \begin{eqnarray} − \widehat{\chi}(\omega) <nowiki>:= 1- \frac{1/x_0 -1}{1/x_0 + j\omega \tau_{d}} = \frac{1+(\tau_{d}\omega)^2x_0+j\omega\tau_{d}(1-x_0)}{1/x_0+(\tau_{d}\omega)^2 x_0}\,. − \tag{18} − \end{eqnarray} − \)</nowiki></span> − </p><p>To interpret the result, we plug into Eq. (<a href="#Eq-17">17</a>) the Fourier transform $\widehat{R}=R_0\delta(\omega)+R_1 \widehat{\rho}$, − which yields − </p><p><span id="Eq-19">\( − \begin{eqnarray} − \widehat{I}(\omega) = I_0 \delta(\omega) + \frac{I_0 R_1}{R_0} \widehat{\chi}(\omega) \widehat{\rho}(\omega)\,. − \tag{19} − \end{eqnarray} − \) − </p><p>Finally, the inverse Fourier transform of Eq. (<a href="#Eq-19">19</a>) reads − <span id="Eq-20">\( − \begin{eqnarray} − I(t) = I_0 + \frac{I_0 R_1}{R_0} \int {\rm d}\tau \, \chi(\tau) \rho(t-\tau) − \tag{20} − \end{eqnarray} − \)</span> − with − <span id="Eq-21"><nowiki>\( − \begin{eqnarray} − \chi(t)=\delta(t) - \frac{1/x_0-1}{\tau_{d}} \begin{cases} \displaystyle {\exp\left(-\frac{t}{x_0\tau_{d}}\right)} </nowiki>& \text{for}\quad t\ge0 \\ 0 & \text{for}\quad t<0 \end{cases}\,. − \tag{21} − \end{eqnarray} − \)</span> − </p><p>Therefore the output current $I$ is the sum of the steady-state current $I_0$ and the filtered perturbation $\frac{I_0 R_1}{R_0} \int {\rm d}\tau \, \chi(\tau) \rho(t-\tau)$ where $\chi$ is the filter we are interested in. − </p> − <h2> <span class="mw-headline" id="References"> References </span></h2> − <ul><li> <span id="ResearchTopic" /> <b>Research Topic</b>: <i>Neural Information Processing with Dynamical Synapses</i>. S. Wu, K. Y. Michael Wong and M. Tsodyks. <i>Frontiers in Computational Neuroscience</i>, 2013 <a rel="nofollow" class="external text" href="http://www.frontiersin.org/Computational_Neuroscience/researchtopics/Neural_Information_Processing_/821">link</a> − </li></ul> − <ul><li><span id="Abbott97">Abbott, L. F. et al (1997).</span> Synaptic Depression and Cortical Gain Control. <em>Science.</em> 275(5297): 221-224. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1126/science.275.5297.221">doi:10.1126/science.275.5297.221</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1126/science.275.5297.221">doi:10.1126/science.275.5297.221</a> − </li><li><span id="Abbott04">Abbott, L. F. and Regehr, Wade G. (2004).</span> Synaptic computation. <em>Nature.</em> 431(7010): 796-803. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1038/nature03010">doi:10.1038/nature03010</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1038/nature03010">doi:10.1038/nature03010</a> − </li><li><span id="Amari77">Amari, Shun-ichi (1977).</span> Dynamics of <a href="/article/Self-organization" title="Self-organization">pattern formation</a> in lateral-inhibition type <a href="/article/Neural_fields" title="Neural fields">neural fields</a>. <em>Biological Cybernetics.</em> 27(2): 77-87. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1007/bf00337259">doi:10.1007/bf00337259</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1007/BF00337259">doi:10.1007/BF00337259</a> − </li><li><span id="Barak07">Barak, Omri and Tsodyks, Misha (2007).</span> Persistent Activity in Neural Networks with Dynamic Synapses. <em>PLoS Computational Biology.</em> 3(2): e35. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1371/journal.pcbi.0030104">doi:10.1371/journal.pcbi.0030104</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1371/journal.pcbi.0030035">doi:10.1371/journal.pcbi.0030035</a> − </li><li> <span id="Bi01" /> G. Bi and M. Poo. Synaptic modification by correlated activity: <a href="/article/Donald_Olding_Hebb" title="Donald Olding Hebb">Hebb</a>’s postulate revisited. Annu. Rev. Neurosci. 24: 139–66, 2001. − </li><li><span id="Bourjaily12">Bourjaily, M. A. and Miller, P. (2012).</span> Dynamic afferent synapses to decision-making networks improve performance in tasks requiring stimulus associations and discriminations. <em>Journal of Neurophysiology.</em> 108(2): 513-527. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1152/jn.00806.2011">doi:10.1152/jn.00806.2011</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1152/jn.00806.2011">doi:10.1152/jn.00806.2011</a> − </li><li> <span id="Bressloff12" /> P. C. Bressloff. Spatiotemporal Dynamics of Continuum Neural Fields J. Phys. A 45, 033001, 2012. − </li><li><span id="Buonomano09">Buonomano, Dean V. and Maass, Wolfgang (2009).</span> State-dependent computations: spatiotemporal processing in cortical networks. <em>Nature Reviews <a href="/article/Neuroscience" title="Neuroscience">Neuroscience</a>.</em> 10(2): 113-125. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1038/nrn2558">doi:10.1038/nrn2558</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1038/nrn2558">doi:10.1038/nrn2558</a> − </li><li><span id="Cook03">Cook, Daniel L.; Schwindt, Peter C.; Grande, Lucinda A. and Spain, William J. (2003).</span> Synaptic depression in the localization of sound. <em>Nature.</em> 421(6918): 66-70. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1038/nature01248">doi:10.1038/nature01248</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1038/nature01248">doi:10.1038/nature01248</a> − </li><li> <span id="Dittman00" /> 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. − </li><li><span id="Fortune01">Fortune, Eric S. and Rose, Gary J. (2001).</span> Short-term synaptic plasticity as a temporal filter. <em>Trends in Neurosciences.</em> 24(7): 381-385. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1016/s0166-2236(00)01835-x">doi:10.1016/s0166-2236(00)01835-x</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1016/S0166-2236(00)01835-X">doi:10.1016/S0166-2236(00)01835-X</a> − </li><li> <span id="Fuhrmann02" /> G. Fuhrmann et al. Coding of Temporal Information by Activity-Dependent Synapses. J. Neurophysiol. 87: 140-148, 2002. − </li><li><span id="Fung12a">Fung, C. C. Alan; Wong, K. Y. Michael; Wang, He and Wu, Si (2012).</span> Dynamical Synapses Enhance Neural Information Processing: Gracefulness, Accuracy, and Mobility. <em>Neural Computation.</em> 24(5): 1147-1185. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1162/neco_a_00269">doi:10.1162/neco_a_00269</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1162/NECO_a_00269">doi:10.1162/NECO_a_00269</a> − </li><li> <span id="Fung12b" /> C. C. Fung, K. Y. Michael Wong and S. Wu. Delay Compensation with Dynamical Synapses. Advances in Neural Information Processing Systems 16, 2012. − </li><li> <span id="Fung13" /> 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. − </li><li><span id="Fuster71">Fuster, J. M. and Alexander, G. E. (1971).</span> Neuron Activity Related to Short-Term Memory. <em>Science.</em> 173(3997): 652-654. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1126/science.173.3997.652">doi:10.1126/science.173.3997.652</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1126/science.173.3997.652">doi:10.1126/science.173.3997.652</a> − </li><li><span id="Goldman02">Goldman, Mark S.; Maldonado, Pedro and Abbott, L. F. (2002).</span> Redundancy Reduction and Sustained Firing with Stochastic Depressing Synapses <em>The Journal of Neuroscience</em> 22(2): 584-591. − </li><li><span id="Holcman06">Holcman, David and Tsodyks, Misha (2006).</span> The Emergence of <a href="/article/Up_and_Down_States" title="Up and Down States">Up and Down States</a> in Cortical Networks. <em>PLoS Computational Biology.</em> 2(3): e23. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1371/journal.pcbi.0020023">doi:10.1371/journal.pcbi.0020023</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1371/journal.pcbi.0020023">doi:10.1371/journal.pcbi.0020023</a> − </li><li> <span id="Igarashi12" /> Y. Igarashi, M. Oizumi and M. Okada. Theory of correlation in a network with synaptic depression. Physical Review E, 85, 016108, 2012. − </li><li><span id="Karmarkar07">Karmarkar, Uma R. and Buonomano, Dean V. (2007).</span> Timing in the Absence of Clocks: Encoding Time in Neural Network States. <em>Neuron.</em> 53(3): 427-438. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1016/j.neuron.2007.01.006">doi:10.1016/j.neuron.2007.01.006</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1016/j.neuron.2007.01.006">doi:10.1016/j.neuron.2007.01.006</a> − </li><li><span id="Katori11">Katori, Yuichi et al. (2011).</span> Representational Switching by Dynamical Reorganization of Attractor Structure in a Network Model of the Prefrontal Cortex. <em>PLoS Computational Biology.</em> 7(11): e1002266. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1371/journal.pcbi.1002266">doi:10.1371/journal.pcbi.1002266</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1371/journal.pcbi.1002266">doi:10.1371/journal.pcbi.1002266</a> − </li><li><span id="Kilpatrick10">Kilpatrick, Zachary P. and Bressloff, Paul C. (2010).</span> <a href="/article/Binocular_Rivalry" title="Binocular Rivalry">Binocular Rivalry</a> in a Competitive Neural Network with Synaptic Depression. <em>SIAM Journal on Applied Dynamical Systems.</em> 9(4): 1303-1347. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1137/100788872">doi:10.1137/100788872</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1137/100788872">doi:10.1137/100788872</a> − </li><li><span id="Klyachko06">Klyachko, Vitaly A. and Stevens, Charles F. (2006).</span> Excitatory and Feed-Forward Inhibitory <a href="/article/Hippocampus" title="Hippocampus">Hippocampal</a> Synapses Work Synergistically as an Adaptive Filter of Natural Spike Trains. <em>PLoS Biology.</em> 4(7): e207. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1371/journal.pbio.0040207">doi:10.1371/journal.pbio.0040207</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1371/journal.pbio.0040207">doi:10.1371/journal.pbio.0040207</a> − </li><li> <span id="Loebel02" /> A. Loebel and M. Tsodyks. Computation by ensemble synchronization in recurrent networks with synaptic depression. J. Comput. Neurosci. 13: 111-124, 2002. − </li><li><span id="Markram98">Markram, H.; Wang, Y. and Tsodyks, M. (1998).</span> Differential signaling via the same axon of neocortical <a href="/article/Pyramidal_neuron" title="Pyramidal neuron">pyramidal neurons</a>. <em>Proceedings of the National Academy of Sciences.</em> 95(9): 5323-5328. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1073/pnas.95.9.5323">doi:10.1073/pnas.95.9.5323</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1073/pnas.95.9.5323">doi:10.1073/pnas.95.9.5323</a> − </li><li><span id="Markram96">Markram, Henry and Tsodyks, Misha (1996).</span> Redistribution of synaptic efficacy between neocortical pyramidal neurons. <em>Nature.</em> 382(6594): 807-810. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1038/382807a0">doi:10.1038/382807a0</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1038/382807a0">doi:10.1038/382807a0</a> − </li><li><span id="Mej.C3.ADas08">Mejías, Jorge F. and Torres, Joaquín J. (2008).</span> The role of synaptic facilitation in spike coincidence detection. <em>Journal of Computational Neuroscience.</em> 24(2): 222-234. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1007/s10827-007-0052-8">doi:10.1007/s10827-007-0052-8</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1007/s10827-007-0052-8">doi:10.1007/s10827-007-0052-8</a> − </li><li><span id="Mej.C3.ADas09">Mejías, Jorge F. and Torres, Joaquín J. (2009).</span> Maximum Memory Capacity on Neural Networks with Short-Term Synaptic Depression and Facilitation. <em>Neural Computation.</em> 21(3): 851-871. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1162/neco.2008.02-08-719">doi:10.1162/neco.2008.02-08-719</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1162/neco.2008.02-08-719">doi:10.1162/neco.2008.02-08-719</a> − </li><li><span id="Melamed08">Melamed, Ofer; Barak, Omri; Silberberg, Gilad; Markram, Henry and Tsodyks, Misha (2008).</span> Slow <a href="/article/Periodic_Orbit" title="Periodic Orbit">oscillations</a> in neural networks with facilitating synapses. <em>Journal of Computational Neuroscience.</em> 25(2): 308-316. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1007/s10827-008-0080-z">doi:10.1007/s10827-008-0080-z</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1007/s10827-008-0080-z">doi:10.1007/s10827-008-0080-z</a> − </li><li><span id="Mongillo08">Mongillo, G.; Barak, O. and Tsodyks, M. (2008).</span> Synaptic Theory of Working Memory. <em>Science.</em> 319(5869): 1543-1546. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1126/science.1150769">doi:10.1126/science.1150769</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1126/science.1150769">doi:10.1126/science.1150769</a> − </li><li><span id="Rosenbaum12">Rosenbaum, Robert; Rubin, Jonathan and Doiron, Brent (2012).</span> Short Term Synaptic Depression Imposes a Frequency Dependent Filter on Synaptic Information Transfer. <em>PLoS Computational Biology.</em> 8(6): e1002557. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1371/journal.pcbi.1002557">doi:10.1371/journal.pcbi.1002557</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1371/journal.pcbi.1002557">doi:10.1371/journal.pcbi.1002557</a> − </li><li><span id="Rotman11">Rotman, Z.; Deng, P.-Y. and Klyachko, V. A. (2011).</span> Short-Term Plasticity Optimizes Synaptic Information Transmission. <em>Journal of Neuroscience.</em> 31(41): 14800-14809. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1523/jneurosci.3231-11.2011">doi:10.1523/jneurosci.3231-11.2011</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1523/JNEUROSCI.3231-11.2011">doi:10.1523/JNEUROSCI.3231-11.2011</a> − </li><li><span id="Stevens95">Stevens, Charles F and Wang, Yanyan (1995).</span> Facilitation and depression at single central synapses. <em>Neuron.</em> 14(4): 795-802. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1016/0896-6273(95)90223-6">doi:10.1016/0896-6273(95)90223-6</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1016/0896-6273(95)90223-6">doi:10.1016/0896-6273(95)90223-6</a> − </li><li><span id="Torres07">Torres, J. J.; Cortes, J. M.; Marro, J. and Kappen, H. J. (2007).</span> Competition Between Synaptic Depression and Facilitation in Attractor Neural Networks. <em>Neural Computation.</em> 19(10): 2739-2755. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1162/neco.2007.19.10.2739">doi:10.1162/neco.2007.19.10.2739</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1162/neco.2007.19.10.2739">doi:10.1162/neco.2007.19.10.2739</a> − </li><li><span id="Tsodyks97">Tsodyks, Misha and Markram, Henry (1997).</span> The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. <em>Proceedings of the National Academy of Sciences.</em> 94(2): 719-723. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1073/pnas.94.2.719">doi:10.1073/pnas.94.2.719</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1073/pnas.94.2.719">doi:10.1073/pnas.94.2.719</a> − </li><li><span id="Tsodyks98">Tsodyks, Misha; Pawelzik, Klaus and Markram, Henry (1998).</span> Neural Networks with Dynamic Synapses. <em>Neural Computation.</em> 10(4): 821-835. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1162/089976698300017502">doi:10.1162/089976698300017502</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1162/089976698300017502">doi:10.1162/089976698300017502</a> − </li><li><span id="Wang06">Wang, Yun et al. (2006).</span> Heterogeneity in the pyramidal network of the medial prefrontal cortex. <em>Nature Neuroscience.</em> 9(4): 534-542. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1038/nn1670">doi:10.1038/nn1670</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1038/nn1670">doi:10.1038/nn1670</a> − </li><li><span id="York09">York, Lawrence Christopher and van Rossum, Mark C. W. (2009).</span> Recurrent networks with short term synaptic depression. <em>Journal of Computational Neuroscience.</em> 27(3): 607-620. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1007/s10827-009-0172-4">doi:10.1007/s10827-009-0172-4</a>.<a rel="nofollow" class="external text" href="http://dx.doi.org/10.1007/s10827-009-0172-4">doi:10.1007/s10827-009-0172-4</a> − </li><li><span id="Zucker02">Zucker, Robert S. and Regehr, Wade G. (2002).</span> Short-Term Synaptic Plasticity. <em>Annual Review of Physiology.</em> 64(1): 355-405. <a rel="nofollow" class="external text" href="http://dx.doi.org/10.1146/annurev.physiol.64.092501.114547">doi:10.1146/annurev.physiol.64.092501.114547</a> − </li></ul> − − <!-- Tidy found serious XHTML errors --> − − <!-- − NewPP limit report − Preprocessor node count: 1182/1000000 − Post‐expand include size: 12642/2097152 bytes − Template argument size: 8908/2097152 bytes − Expensive parser function count: 0/100 − ExtLoops count: 0/100 − --> − <div class="cp-footer"><table cellpadding="0" border="0"><tr><td>Sponsored by: <a href="/article/User:Eugene_M._Izhikevich" title="User:Eugene M. Izhikevich"><span>Eugene M. Izhikevich</span>, <span>Editor-in-Chief of Scholarpedia, the peer-reviewed open-access encyclopedia</span></a></td></tr><tr><td><a rel="nofollow" class="external text" href="http://www.scholarpedia.org/w/index.php?title=Short-term_synaptic_plasticity&amp;oldid=134980">Reviewed by</a>: <a href="/article/User:Boris_Gutkin" title="User:Boris Gutkin"><span>Prof. Boris Gutkin</span>, <span>(1) Group for Neural Theory, LNC INSERM U960, Département d'Études Cognitives, École Normale Supérieure, Paris, France; (2) Faculty of Psychology, HIgher Shcool of Economics, Moscow, Russia</span></a></td></tr><tr><td><a rel="nofollow" class="external text" href="http://www.scholarpedia.org/w/index.php?title=Short-term_synaptic_plasticity&amp;oldid=135887">Reviewed by</a>: <a href="/article/User:Stefano_Fusi" title="User:Stefano Fusi"><span>Dr. Stefano Fusi</span>, <span>Institute of Neuroinformatics, University of Zurich, Switzerland</span></a></td></tr><tr><td>Accepted on: <a rel="nofollow" class="external text" href="http://www.scholarpedia.org/w/index.php?title=Short-term_synaptic_plasticity&amp;oldid=136876">2013-10-11 01:17:19 GMT</a></td></tr></table></div> − </div> <!-- /bodycontent --> − <!-- printfooter --> − <div class="printfooter"> − Retrieved from "<a href="http://www.scholarpedia.org/w/index.php?title=Short-term_synaptic_plasticity&amp;oldid=182521">http://www.scholarpedia.org/w/index.php?title=Short-term_synaptic_plasticity&oldid=182521</a>" </div> − <!-- /printfooter --> − <!-- catlinks --> − <div id="catlinks" class="catlinks"><div class="mw-normal-catlinks"><a href="/article/Special:Categories" title="Special:Categories">Category</a>: <ul><li><a href="/article/Category:Neuroscience" title="Category:Neuroscience">Neuroscience</a></li></ul></div></div> <!-- /catlinks --> − <div class="visualClear"></div> − <!-- debughtml --> − <!-- /debughtml --> − </div> − <!-- /bodyContent --> − </div> − <!-- /content --> − <!-- header --> − <div class="noprint"> − − <!-- 0 --> − <div id="p-personal" class=""> − <h5>Personal tools</h5> − <ul> − <li id="pt-login"><nowiki><a href="/w/index.php?title=Special:UserLogin&amp;returnto=Short-term+synaptic+plasticity" title="You are encouraged to log in; however, it is not mandatory [o]" accesskey="o">Log in / create account</a></nowiki></li> − </ul> − </div> − − <!-- /0 --> − <div id="left-navigation"> − − <!-- 0 --> − <div id="p-namespaces" class="vectorTabs"> − <h5>Namespaces</h5> − <ul> − <li id="ca-nstab-main" class="selected"><span><a href="/article/Short-term_synaptic_plasticity" title="View the content page [c]" accesskey="c">Page</a></span></li> − <li id="ca-talk"><span><a href="/article/Talk:Short-term_synaptic_plasticity" title="Discussion about the content page [t]" accesskey="t">Discussion</a></span></li> − </ul> − </div> − − <!-- /0 --> − − <!-- 1 --> − <div id="p-variants" class="vectorMenu emptyPortlet"> − <h4> </h4> − <h5><span>Variants</span><a href="#"></a></h5> − <div class="menu"> − <ul> </ul> − </div> − </div> − − <!-- /1 --> − </div> − <div id="right-navigation"> − − <!-- 0 --> − <div id="p-views" class="vectorTabs"> − <h5>Views</h5> − <ul> − <li id="ca-view" class="selected"><span><a href="/article/Short-term_synaptic_plasticity" >Read</a></span></li> − <li id="ca-viewsource"><span><nowiki><a href="/w/index.php?title=Short-term_synaptic_plasticity&amp;action=edit" title="This page is protected.&#10;You can view its source [e]" accesskey="e">View source</a></nowiki></span></li> − <li id="ca-history" class="collapsible"><span><nowiki><a href="/w/index.php?title=Short-term_synaptic_plasticity&amp;action=history" title="Past revisions of this page [h]" accesskey="h">View history</a></nowiki></span></li> − </ul> − </div> − − <!-- /0 --> − − <!-- 1 --> − <div id="p-cactions" class="vectorMenu emptyPortlet"> − <h5><span>Actions</span><a href="#"></a></h5> − <div class="menu"> − <ul> </ul> − </div> − </div> − − <!-- /1 --> − − <!-- 2 --> − <div id="p-search"> − <h5><label for="searchInput">Search</label></h5> − <form action="/w/index.php" id="searchform"> − <div id="simpleSearch"> − <input name="search" title="Search Scholarpedia [f]" accesskey="f" id="searchInput" /> <button name="button" title="Search the pages for this text" id="searchButton"><img src="/w/skins/vector/images/search-ltr.png?303" alt="Search" /></button> <input type='hidden' name="title" value="Special:Search"/> − </div> − </form> − </div> − − <!-- /2 --> − </div> − </div> − <!-- /header --> − <!-- panel --> − <div class="noprint"> − <!-- logo --> − <div id="p-logo"><a style="background-image: url(/w/skins/vector/images/splogo.png);" href="/article/Main_Page" title="Visit the main page"></a></div> − <!-- /logo --> − − <!-- navigation --> − <div class="portal" id="p-navigation"> − <h5>Navigation</h5> − <div class="body"> − <ul> − <li id="n-mainpage-description"><a href="/article/Main_Page" title="Visit the main page [z]" accesskey="z">Main page</a></li> − <li id="n-About"><a href="/article/Scholarpedia:About">About</a></li> − <li id="n-Propose-a-new-article"><a href="/article/Special:ProposeArticle">Propose a new article</a></li> − <li id="n-Instructions-for-Authors"><a href="/article/Scholarpedia:Instructions_for_Authors">Instructions for Authors</a></li> − <li id="n-randompage"><a href="/article/Special:Random" title="Load a random page [x]" accesskey="x">Random article</a></li> − <li id="n-FAQs"><a href="/article/Help:Frequently_Asked_Questions">FAQs</a></li> − <li id="n-Help"><a href="/article/Scholarpedia:Help">Help</a></li> − </ul> − </div> − </div> − − <!-- /navigation --> − − <!-- Focal areas --> − <div class="portal" id="p-Focal_areas"> − <h5>Focal areas</h5> − <div class="body"> − <ul> − <li id="n-Astrophysics"><a href="/article/Encyclopedia:Astrophysics">Astrophysics</a></li> − <li id="n-Celestial-mechanics"><a href="/article/Encyclopedia:Celestial_Mechanics">Celestial mechanics</a></li> − <li id="n-Computational-neuroscience"><a href="/article/Encyclopedia:Computational_neuroscience">Computational neuroscience</a></li> − <li id="n-Computational-intelligence"><a href="/article/Encyclopedia:Computational_intelligence">Computational intelligence</a></li> − <li id="n-Dynamical-systems"><a href="/article/Encyclopedia:Dynamical_systems">Dynamical systems</a></li> − <li id="n-Physics"><a href="/article/Encyclopedia:Physics">Physics</a></li> − <li id="n-Touch"><a href="/article/Encyclopedia:Touch">Touch</a></li> − <li id="n-More-topics"><a href="/article/Scholarpedia:Topics">More topics</a></li> − </ul> − </div> − </div> − − <!-- /Focal areas --> − − <!-- Activity --> − <div class="portal" id="p-Activity"> − <h5>Activity</h5> − <div class="body"> − <ul> − <li id="n-Recently-published-articles"><a href="/article/Special:RecentlyPublished">Recently published articles</a></li> − <li id="n-Recently-sponsored-articles"><a href="/article/Special:RecentlySponsored">Recently sponsored articles</a></li> − <li id="n-recentchanges"><a href="/article/Special:RecentChanges" title="A list of recent changes in the wiki [r]" accesskey="r">Recent changes</a></li> − <li id="n-All-articles"><a href="/article/Special:AllPages">All articles</a></li> − <li id="n-List-all-Curators"><a href="/article/Special:ListCurators">List all Curators</a></li> − <li id="n-List-all-users"><a href="/article/Special:ListUsers">List all users</a></li> − <li id="n-Journal"><a href="/article/Special:Journal">Scholarpedia Journal</a></li> − </ul> − </div> − </div> − − <!-- /Activity --> − − <!-- SEARCH --> − − <!-- /SEARCH --> − − <!-- TOOLBOX --> − <div class="portal" id="p-tb"> − <h5>Tools</h5> − <div class="body"> − <ul> − <li id="t-whatlinkshere"><a href="/article/Special:WhatLinksHere/Short-term_synaptic_plasticity" title="A list of all wiki pages that link here [j]" accesskey="j">What links here</a></li> − <li id="t-recentchangeslinked"><a href="/article/Special:RecentChangesLinked/Short-term_synaptic_plasticity" title="Recent changes in pages linked from this page [k]" accesskey="k">Related changes</a></li> − <li id="t-specialpages"><a href="/article/Special:SpecialPages" title="A list of all special pages [q]" accesskey="q">Special pages</a></li> − <li><a href="/w/index.php?title=Short-term_synaptic_plasticity&amp;printable=yes" rel="alternate">Printable version</a></li> − <li id="t-permalink"><a href="/w/index.php?title=Short-term_synaptic_plasticity&amp;oldid=182521" title="Permanent link to this revision of the page">Permanent link</a></li> − </ul> − </div> − </div> − − <!-- /TOOLBOX --> − − <!-- LANGUAGES --> − − <!-- /LANGUAGES --> − − </div> − <!-- /panel --> − <!-- footer --> − <div id="footer"> − − − <div id="footer-icons"> − <ul class="social"> − <li><a href="https://twitter.com/scholarpedia" target="_blank"><img src="/w/skins/vector/images/twitter.png?303" /></a></li> − <li><a href="https://plus.google.com/112873162496270574424" target="_blank"><img src="https://ssl.gstatic.com/images/icons/gplus-16.png" /></a></li> − <li><a href="http://www.facebook.com/Scholarpedia" target="_blank"><img src="/w/skins/vector/images/facebook.png?303" /></a></li> − <li><a href="http://www.linkedin.com/groups/Scholarpedia-4647975/about" target="_blank"><img src="/w/skins/vector/images/linkedin.png?303" /></a></li> − </ul> − − <ul id="footer-icons" class="noprint"> − <li id="footer-poweredbyico"> − <a href="http://www.mediawiki.org/"><img src="/w/skins/common/images/poweredby_mediawiki_88x31.png" alt="Powered by MediaWiki" width="88" height="31" /></a> <a href="http://www.mathjax.org/"><img src="/w/skins/common/images/MathJaxBadge.gif" alt="Powered by MathJax" width="88" height="31" /></a> <a href="http://creativecommons.org/licenses/by-nc-sa/3.0/deed.en_US"><img src="/w/skins/common/88x31.png" alt="Creative Commons License" width="88" height="31" /></a> </li> − </ul> − </div> − − <ul id="footer-info"> − <li id="footer-info-lastmod"> This page was last modified on 20 April 2017, at 08:39.</li> − <li id="footer-info-viewcount">This page has been accessed 121,524 times.</li> − <li id="footer-info-copyright"> − <span xmlns:dct="http://purl.org/dc/terms/" property="dct:title">"Short-term synaptic plasticity"</span> by − <a xmlns:cc="http://creativecommons.org/ns#" href="http://www.scholarpedia.org/article/Short-term_synaptic_plasticity" property="cc:attributionName" rel="cc:attributionURL"> − Misha Tsodyks and Si Wu − </a> is licensed under a − <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/3.0/deed.en_US"> − Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License</a>. Permissions beyond the scope of this license are described in the <a xmlns:cc="http://creativecommons.org/ns#" href="http://www.scholarpedia.org/article/Scholarpedia:Terms_of_use" rel="cc:morePermissions">Terms of Use</a></li> − </ul> − <ul id="footer-places"> − <li id="footer-places-privacy"><a href="/article/Scholarpedia:Privacy_policy" title="Scholarpedia:Privacy policy">Privacy policy</a></li> − <li id="footer-places-about"><a href="/article/Scholarpedia:About" class="mw-redirect" title="Scholarpedia:About">About Scholarpedia</a></li> − <li id="footer-places-disclaimer"><a href="/article/Scholarpedia:General_disclaimer" title="Scholarpedia:General disclaimer">Disclaimers</a></li> − </ul> − − <div style="clear:both"></div> − </div> − <!-- /footer --> − <script src="http://www.scholarpedia.org/w/load.php?debug=false&amp;lang=en&amp;modules=skins.vector&amp;only=scripts&amp;skin=vector&amp;*"></script> − <script>if(window.mw){ − mw.loader.load(["jquery.ui.dialog","curatorpedia.dashboard","curatorpedia.confirm","mediawiki.user","mediawiki.page.ready","ext.vector.collapsibleNav","ext.vector.collapsibleTabs","ext.vector.simpleSearch"], null, true); − }</script> − <script> − var wgSitename = 'http://www.scholarpedia.org';</script> − − <script type='text/x-mathjax-config'> − //<![CDATA[ − MathJax.Hub.Config({ − styles: { − ".MathJax_Display": { − display: "table-cell ! important", − padding: "1em 0 ! important", − width: (MathJax.Hub.Browser.isMSIE && (document.documentMode||0) < 8 ? − "100% ! important" : "1000em ! important") − } − }, − extensions: ["tex2jax.js","TeX/noErrors.js", "TeX/AMSmath.js","TeX/AMSsymbols.js"], − jax: ["input/TeX", "output/HTML-CSS"], − tex2jax: { − inlineMath: [ ['$','$'], ["\\(","\\)"] ], − displayMath: [ ['$$','$$'], ["\\[","\\]"] ], − processEscapes: false, − element: "content", − ignoreClass: "(tex2jax_ignore|mw-search-results|searchresults)", /* note: this is part of a regex, check the docs! */ − skipTags: ["script","noscript","style","textarea","code"] /* removed pre as wikimedia renders math in there */ − }, − TeX: { − Macros: { − /* Wikipedia compatibility: these macros are used on Wikipedia */ − empty: '\\emptyset', − P: '\\unicode{xb6}', − Alpha: '\\unicode{x391}', /* FIXME: These capital Greeks don't show up in bold in \boldsymbol ... */ − Beta: '\\unicode{x392}', − Epsilon: '\\unicode{x395}', − Zeta: '\\unicode{x396}', − Eta: '\\unicode{x397}', − Iota: '\\unicode{x399}', − Kappa: '\\unicode{x39a}', − Mu: '\\unicode{x39c}', − Nu: '\\unicode{x39d}', − Pi: '\\unicode{x3a0}', − Rho: '\\unicode{x3a1}', − Sigma: '\\unicode{x3a3}', − Tau: '\\unicode{x3a4}', − Chi: '\\unicode{x3a7}', − C: '\\mathbb{C}', /* the complex numbers */ − N: '\\mathbb{N}', /* the natural numbers */ − Q: '\\mathbb{Q}', /* the rational numbers */ − R: '\\mathbb{R}', /* the real numbers */ − Z: '\\mathbb{Z}', /* the integer numbers */ − − /* some extre macros for ease of use; these are non-standard! */ − F: '\\mathbb{F}', /* a finite field */ − HH: '\\mathcal{H}', /* a Hilbert space */ − bszero: '\\boldsymbol{0}', /* vector of zeros */ − bsone: '\\boldsymbol{1}', /* vector of ones */ − bst: '\\boldsymbol{t}', /* a vector 't' */ − bsv: '\\boldsymbol{v}', /* a vector 'v' */ − bsw: '\\boldsymbol{w}', /* a vector 'w' */ − bsx: '\\boldsymbol{x}', /* a vector 'x' */ − bsy: '\\boldsymbol{y}', /* a vector 'y' */ − bsz: '\\boldsymbol{z}', /* a vector 'z' */ − bsDelta: '\\boldsymbol{\\Delta}', /* a vector '\Delta' */ − E: '\\mathrm{e}', /* the exponential */ − rd: '\\,\\mathrm{d}', /* roman d for use in integrals: $\int f(x) \rd x$ */ − rdelta: '\\,\\delta', /* delta operator for use in sums */ − rD: '\\mathrm{D}', /* differential operator D */ − − /* example from MathJax on how to define macros with parameters: */ − /* bold: ['{\\bf #1}', 1] */ − − RR: '\\mathbb{R}', − ZZ: '\\mathbb{Z}', − NN: '\\mathbb{N}', − QQ: '\\mathbb{Q}', − CC: '\\mathbb{C}', − FF: '\\mathbb{F}' − } − } − }); − //]]> − //<![CDATA[ − MathJax.Hub.config.tex2jax.inlineMath.push(['$','$']); − MathJax.Hub.config.tex2jax.displayMath.push(['$$','$$']); − //]]> − </script> − − <script type='text/javascript' src='https://cdn.mathjax.org/mathjax/2.3-latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'></script> − − <script src="http://www.scholarpedia.org/w/load.php?debug=false&amp;lang=en&amp;modules=site&amp;only=scripts&amp;skin=vector&amp;*"></script> − <script type="text/javascript"> − var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www."); − document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E")); − </script> − <script type="text/javascript"> − var pageTracker = _gat._getTracker("UA-22078568-1"); − pageTracker._initData(); − pageTracker._trackPageview(); − </script><!-- Served in 0.751 secs. --> − − </body> − </html> − 第733行：
第44行： − + 第822行：
第133行： − + 第835行：
第146行： − + 第944行：
第255行： − + 第963行：
第274行： − + 第973行：
第284行： − + − +
无编辑摘要
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
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
*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
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)。
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)。
===Gain control===
===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.
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.
图2。(A)一个以std为主的突触的有效性和它产生的突触后电流的稳定值，分别由和测量。参数如图1b所示。(B)对于stf为主的突触，与(A)相同。参数与图1C相同。(C) std主导突触的过滤特性，由[Eq.(6)]测量。(D)对突然输入变化的神经反应vs. std主导突触的稳定速率值。赫兹。参数如图1b所示。
图2。(A)一个以std为主的突触的有效性和它产生的突触后电流的稳定值，分别由和测量。参数如图1b所示。(B)对于stf为主的突触，与(A)相同。参数与图1C相同。(C) std主导突触的过滤特性，由[Eq.(6)]测量。(D)对突然输入变化的神经反应vs. std主导突触的稳定速率值。赫兹。参数如图1b所示。
==Effects on network dynamics==
== Effects on network dynamics==
In addition to feedforward and feedback transmission, neural circuits generate recurrent interactions between neurons. With STP included in the recurrent interactions, the network dynamics exhibits many new interesting behaviors that do not arise with purely static synapses. These new dynamical properties could therefore implement STP-mediated network computation.
In addition to feedforward and feedback transmission, neural circuits generate recurrent interactions between neurons. With STP included in the recurrent interactions, the network dynamics exhibits many new interesting behaviors that do not arise with purely static synapses. These new dynamical properties could therefore implement STP-mediated network computation.
*Abbott, L. F. et al (1997). Synaptic Depression and Cortical Gain Control. ''Science.'' 275(5297): 221-224. doi:10.1126/science.275.5297.221.doi:10.1126/science.275.5297.221
*Abbott, L. F. et al (1997). Synaptic Depression and Cortical Gain Control. ''Science.'' 275(5297): 221-224. doi:10.1126/science.275.5297.221.doi:10.1126/science.275.5297.221
*Abbott, L. F. and Regehr, Wade G. (2004). Synaptic computation. ''Nature.'' 431(7010): 796-803. doi:10.1038/nature03010.doi:10.1038/nature03010
* Abbott, L. F. and Regehr, Wade G. (2004). Synaptic computation. ''Nature.'' 431(7010): 796-803. doi:10.1038/nature03010.doi:10.1038/nature03010
*Amari, Shun-ichi (1977). Dynamics of pattern formation in lateral-inhibition type neural fields. ''Biological Cybernetics.'' 27(2): 77-87. doi:10.1007/bf00337259.doi:10.1007/BF00337259
*Amari, Shun-ichi (1977). Dynamics of pattern formation in lateral-inhibition type neural fields. ''Biological Cybernetics.'' 27(2): 77-87. doi:10.1007/bf00337259.doi:10.1007/BF00337259
*Barak, Omri and Tsodyks, Misha (2007). Persistent Activity in Neural Networks with Dynamic Synapses. ''PLoS Computational Biology.'' 3(2): e35. doi:10.1371/journal.pcbi.0030104.doi:10.1371/journal.pcbi.0030035
*Barak, Omri and Tsodyks, Misha (2007). Persistent Activity in Neural Networks with Dynamic Synapses. ''PLoS Computational Biology.'' 3(2): e35. doi:10.1371/journal.pcbi.0030104.doi:10.1371/journal.pcbi.0030035
*Y. Igarashi, M. Oizumi and M. Okada. Theory of correlation in a network with synaptic depression. Physical Review E, 85, 016108, 2012.
*Y. Igarashi, M. Oizumi and M. Okada. Theory of correlation in a network with synaptic depression. Physical Review E, 85, 016108, 2012.
*Karmarkar, Uma R. and Buonomano, Dean V. (2007). Timing in the Absence of Clocks: Encoding Time in Neural Network States. ''Neuron.'' 53(3): 427-438. doi:10.1016/j.neuron.2007.01.006.doi:10.1016/j.neuron.2007.01.006
*Karmarkar, Uma R. and Buonomano, Dean V. (2007). Timing in the Absence of Clocks: Encoding Time in Neural Network States. ''Neuron.'' 53(3): 427-438. doi:10.1016/j.neuron.2007.01.006.doi:10.1016/j.neuron.2007.01.006
*Katori, Yuichi et al. (2011). Representational Switching by Dynamical Reorganization of Attractor Structure in a Network Model of the Prefrontal Cortex. ''PLoS Computational Biology.'' 7(11): e1002266. doi:10.1371/journal.pcbi.1002266.doi:10.1371/journal.pcbi.1002266
* Katori, Yuichi et al. (2011). Representational Switching by Dynamical Reorganization of Attractor Structure in a Network Model of the Prefrontal Cortex. ''PLoS Computational Biology.'' 7(11): e1002266. doi:10.1371/journal.pcbi.1002266.doi:10.1371/journal.pcbi.1002266
*Kilpatrick, Zachary P. and Bressloff, Paul C. (2010). Binocular Rivalry in a Competitive Neural Network with Synaptic Depression. ''SIAM Journal on Applied Dynamical Systems.'' 9(4): 1303-1347. doi:10.1137/100788872.doi:10.1137/100788872
*Kilpatrick, Zachary P. and Bressloff, Paul C. (2010). Binocular Rivalry in a Competitive Neural Network with Synaptic Depression. ''SIAM Journal on Applied Dynamical Systems.'' 9(4): 1303-1347. doi:10.1137/100788872.doi:10.1137/100788872
*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
*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
*Melamed, Ofer; Barak, Omri; Silberberg, Gilad; Markram, Henry and Tsodyks, Misha (2008). Slow oscillations in neural networks with facilitating synapses. ''Journal of Computational Neuroscience.'' 25(2): 308-316. doi:10.1007/s10827-008-0080-z.doi:10.1007/s10827-008-0080-z
*Melamed, Ofer; Barak, Omri; Silberberg, Gilad; Markram, Henry and Tsodyks, Misha (2008). Slow oscillations in neural networks with facilitating synapses. ''Journal of Computational Neuroscience.'' 25(2): 308-316. doi:10.1007/s10827-008-0080-z.doi:10.1007/s10827-008-0080-z
*Mongillo, G.; Barak, O. and Tsodyks, M. (2008). Synaptic Theory of Working Memory. ''Science.'' 319(5869): 1543-1546. doi:10.1126/science.1150769.doi:10.1126/science.1150769
*Mongillo, G.; Barak, O. and Tsodyks, M. (2008). Synaptic Theory of Working Memory. ''Science.'' 319(5869): 1543-1546. doi:10.1126/science.1150769.doi:10.1126/science.1150769
*Rosenbaum, Robert; Rubin, Jonathan and Doiron, Brent (2012). Short Term Synaptic Depression Imposes a Frequency Dependent Filter on Synaptic Information Transfer. ''PLoS Computational Biology.'' 8(6): e1002557. doi:10.1371/journal.pcbi.1002557.doi:10.1371/journal.pcbi.1002557
* Rosenbaum, Robert; Rubin, Jonathan and Doiron, Brent (2012). Short Term Synaptic Depression Imposes a Frequency Dependent Filter on Synaptic Information Transfer. ''PLoS Computational Biology.'' 8(6): e1002557. doi:10.1371/journal.pcbi.1002557.doi:10.1371/journal.pcbi.1002557
*Rotman, Z.; Deng, P.-Y. and Klyachko, V. A. (2011). Short-Term Plasticity Optimizes Synaptic Information Transmission. ''Journal of Neuroscience.'' 31(41): 14800-14809. doi:10.1523/jneurosci.3231-11.2011.doi:10.1523/JNEUROSCI.3231-11.2011
*Rotman, Z.; Deng, P.-Y. and Klyachko, V. A. (2011). Short-Term Plasticity Optimizes Synaptic Information Transmission. ''Journal of Neuroscience.'' 31(41): 14800-14809. doi:10.1523/jneurosci.3231-11.2011.doi:10.1523/JNEUROSCI.3231-11.2011
*Stevens, Charles F and Wang, Yanyan (1995). Facilitation and depression at single central synapses. ''Neuron.'' 14(4): 795-802. doi:10.1016/0896-6273(95)90223-6.doi:10.1016/0896-6273(95)90223-6
*Stevens, Charles F and Wang, Yanyan (1995). Facilitation and depression at single central synapses. ''Neuron.'' 14(4): 795-802. doi:10.1016/0896-6273(95)90223-6.doi:10.1016/0896-6273(95)90223-6
*Torres, J. J.; Cortes, J. M.; Marro, J. and Kappen, H. J. (2007). Competition Between Synaptic Depression and Facilitation in Attractor Neural Networks. ''Neural Computation.'' 19(10): 2739-2755. doi:10.1162/neco.2007.19.10.2739.doi:10.1162/neco.2007.19.10.2739
*Torres, J. J.; Cortes, J. M.; Marro, J. and Kappen, H. J. (2007). Competition Between Synaptic Depression and Facilitation in Attractor Neural Networks. ''Neural Computation.'' 19(10): 2739-2755. doi:10.1162/neco.2007.19.10.2739.doi:10.1162/neco.2007.19.10.2739
*Tsodyks, Misha and Markram, Henry (1997). The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. ''Proceedings of the National Academy of Sciences.'' 94(2): 719-723. doi:10.1073/pnas.94.2.719.doi:10.1073/pnas.94.2.719
* Tsodyks, Misha and Markram, Henry (1997). The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. ''Proceedings of the National Academy of Sciences.'' 94(2): 719-723. doi:10.1073/pnas.94.2.719.doi:10.1073/pnas.94.2.719
*Tsodyks, Misha; Pawelzik, Klaus and Markram, Henry (1998). Neural Networks with Dynamic Synapses. ''Neural Computation.'' 10(4): 821-835. doi:10.1162/089976698300017502.doi:10.1162/089976698300017502
*Tsodyks, Misha; Pawelzik, Klaus and Markram, Henry (1998). Neural Networks with Dynamic Synapses. ''Neural Computation.'' 10(4): 821-835. doi:10.1162/089976698300017502.doi:10.1162/089976698300017502
*Wang, Yun et al. (2006). Heterogeneity in the pyramidal network of the medial prefrontal cortex. ''Nature Neuroscience.'' 9(4): 534-542. doi:10.1038/nn1670.doi:10.1038/nn1670
*Wang, Yun et al. (2006). Heterogeneity in the pyramidal network of the medial prefrontal cortex. ''Nature Neuroscience.'' 9(4): 534-542. doi:10.1038/nn1670.doi:10.1038/nn1670