https://wiki.swarma.org/api.php?hidebots=1&hideminor=1&urlversion=1&days=30&limit=50&action=feedrecentchanges&feedformat=atom
集智百科 - 复杂系统|人工智能|复杂科学|复杂网络|自组织 - 最近更改 [zh-cn]
2024-03-29T15:07:55Z
用这个源跟踪本wiki的最近更改。
MediaWiki 1.35.0
https://wiki.swarma.org/index.php/%E7%94%A8%E6%88%B7:Yk0071215
用户:Yk0071215
2024-03-20T08:03:43Z
<p>用户账户<a href="/index.php?title=%E7%94%A8%E6%88%B7:Yk0071215&action=edit&redlink=1" class="new mw-userlink" title="用户:Yk0071215(页面不存在)"><bdi>Yk0071215</bdi></a>被<a href="/index.php?title=%E7%94%A8%E6%88%B7:Leshell0609&action=edit&redlink=1" class="new mw-userlink" title="用户:Leshell0609(页面不存在)"><bdi>Leshell0609</bdi></a>创建 《复杂》项目组</p>
Leshell0609
https://wiki.swarma.org/index.php/%E7%94%A8%E6%88%B7:Leshell0609
用户:Leshell0609
2024-03-20T07:51:55Z
<p><a href="/index.php?title=%E7%94%A8%E6%88%B7:Swarma&action=edit&redlink=1" class="new mw-userlink" title="用户:Swarma(页面不存在)"><bdi>Swarma</bdi></a>已将<a href="/index.php?title=%E7%94%A8%E6%88%B7:Leshell0609&action=edit&redlink=1" class="new" title="用户:Leshell0609(页面不存在)">Leshell0609</a>的用户组从(无)更改至管理员 灵箫</p>
Swarma
https://wiki.swarma.org/index.php?title=Sloppy_Model&diff=34376&oldid=34328
Sloppy Model
2024-03-05T14:45:06Z
<p></p>
<table class="diff diff-contentalign-left diff-editfont-monospace" data-mw="interface">
<col class="diff-marker" />
<col class="diff-content" />
<col class="diff-marker" />
<col class="diff-content" />
<tr class="diff-title" lang="zh-Hans-CN">
<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">←上一版本</td>
<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">2024年3月5日 (二) 14:45的版本</td>
</tr><tr><td colspan="4" class="diff-multi" lang="zh-Hans-CN">(未显示同一用户的25个中间版本)</td></tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1" >第1行:</td>
<td colspan="2" class="diff-lineno">第1行:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Sloppiness与Sloppy理论 ==</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Sloppiness与Sloppy理论 ==</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>sloppiness是多参数系统的中常见的一种特性。具有这种特性的模型的参数往往有很多个,但是模型的行为仅取决于少数几个参数或参数的线性组合,其它参数或参数 <del class="diffchange diffchange-inline">的线性组合对模型的影响微乎其微。sloppiness特性在系统生物学、物理学和数学系统中无处不在。</del></div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>sloppiness是多参数系统的中常见的一种特性。具有这种特性的模型的参数往往有很多个,但是模型的行为仅取决于少数几个参数或参数的线性组合,其它参数或参数 <ins class="diffchange diffchange-inline">的线性组合对模型的影响微乎其微。sloppiness特性在系统生物学<ref name = "Gutenkunst et.al, 2007”>Sloppy models and parameter indeterminacy in systems biology: [http://arxiv.org/abs/q-bio/0701039 "Universally Sloppy Parameter Sensitivities in Systems Biology"], Ryan N. Gutenkunst, Joshua J. Waterfall, Fergal P. Casey, Kevin S. Brown, Christopher R. Myers, James P. Sethna, PLoS Comput Biol3(10) e189 (2007). ([http://compbiol.plosjournals.org/perlserv/?request=get-document&doi=10.1371/journal.pcbi.0030189 PLoS], [http://dx.doi.org/10.1371/journal.pcbi.0030189 doi:10.1371/journal.pcbi.0030189]), [https://sethna.lassp.cornell.edu/pubPDF/SloppyEverywhere.pdf pdf]). [Reviewed in [http://biomedicalcomputationreview.org/4/1/4.pdf NewsBytes] of [http://biomedicalcomputationreview.org/ Biomedical Computation Review] (Winter 07/08); rated "Exceptional" on Faculty of 1000]. </ref></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline"><ref name = “Transtrum et.al, 2015”>Sloppiness, information geometry, and model reduction: [http://arxiv.org/abs/1501.07668 Perspective: Sloppiness and Emergent Theories in Physics, Biology, and Beyond], Mark K. Transtrum, Benjamin B. Machta, Kevin S. Brown, Bryan C. Daniels, Christopher R. Myers, and James P. Sethna, [http://scitation.aip.org/content/aip/journal/jcp/143/1/10.1063/1.4923066 J. Chem. Phys. '''143''', 010901 (2015)], </ref><ref name = “Manatee et.al, 2016”>[http://gutengroup.mcb.arizona.edu/wp-content/uploads/Mannakee2016.pdf Sloppiness and the Geometry of Parameter Space.] In: Geris, L., Gomez-Cabrero, D. (eds) Mannakee, B.K., Ragsdale, A.P., Transtrum, M.K., Gutenkunst, R.N. (2016).[http://dx.doi.org/10.1007/978-3-319-21296-8_11 Uncertainty in Biology. Studies in Mechanobiology, Tissue Engineering and Biomaterials], vol 17. Springer, Cham. https://doi.org/10.1007/978-3-319-21296-8_11 </ref>、物理学<ref>[https://sethna.lassp.cornell.edu/pubPDF/PC12.pdf "The Statistical Mechanics of Complex Signaling Networks: Nerve Growth Factor Signaling"], Kevin S. Brown, Colin C. Hill, Guillermo A. Calero, Christopher R. Myers, Kelvin H. Lee, James P. Sethna, and Richard A. Cerione, Physical Biology '''1''', 184-195 (2004), with [https://sethna.lassp.cornell.edu/pubPDF/PC12Supporting.pdf supplemental material]. </ref>和数学<ref name = “Brown & Sethna, 2003”> [https://sethna.lassp.cornell.edu/pubPDF/SloppyModelPRE.pdf “Statistical Mechanics Approaches to Models with Many Poorly Known Parameters"], Kevin S. Brown and James P. Sethna, Phys. Rev. E '''68''', 021904 (2003). </ref>系统中无处不在。</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">几年前,在研究细胞内外信号传递过程中蛋白质相互作用机制时,几名物理和生物领域的科学家建立了一个有48个参数的模型,模型中参数之间难以独立分离,且参数变化范围都超过50倍。在面对一个参数不确定性如此之大的复杂模型时,一位生物学家却指出:根据研究经验,模型的实验结果甚至不用电脑就可以估算出来。因为系统的行为与大多数参数不确定性之间的关系并不紧密。</del></div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">几年前,在研究细胞内外信号传递过程中蛋白质相互作用机制<ref>Formulation, application to fitting algorithms: [http://arxiv.org/abs/0909.3884 "Why are nonlinear fits to data so challenging?"], Mark K. Transtrum, Benjamin B. Machta, and James P. Sethna, [http://link.aps.org/doi/10.1103/PhysRevLett.104.060201 Phys. Rev. Lett.] '''104''', 060201 (2010).</ref><ref>Expanded formulation, geometry of model manifold: [http://arxiv.org/abs/1010.1449 "Geometry of nonlinear least squares with applications to sloppy models and optimization"], Mark K. Transtrum, Benjamin B. Machta, and James P. Sethna Phys. Rev. E '''83''', 036701 (2011); </ref>时,几名物理和生物领域的科学家建立了一个有48个参数的模型,模型中参数之间难以独立分离,且参数变化范围都超过50倍。在面对一个参数不确定性如此之大的复杂模型时,一位生物学家却指出:根据研究经验,模型的实验结果甚至不用电脑就可以估算出来。因为系统的行为与大多数参数不确定性之间的关系并不紧密。</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>大量的生物模型都存在类似的现象,例如生物钟的模型。该模型有36个参数,参数空间中符合模型行为特征的参数点构成了一个线性空间,把参数空间向某一平面投影,如图所示。在图上可以看出有实验点分布的方向是sloppy(“欠定”)的(即沿着这个方向变化参数,模型的行为不会发生明显改变),而垂直实验点分布方向是stiff(“僵硬”)的(即在这个方向上改变模型参数,模型的行为会发生显著变化),而在垂直于这个平面的参数空间中,大部分方向都是sloppy(“欠定”)的。</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>大量的生物模型都存在类似的现象,例如生物钟的模型。该模型有36个参数,参数空间中符合模型行为特征的参数点构成了一个线性空间,把参数空间向某一平面投影,如图所示。在图上可以看出有实验点分布的方向是sloppy(“欠定”)的(即沿着这个方向变化参数,模型的行为不会发生明显改变),而垂直实验点分布方向是stiff(“僵硬”)的(即在这个方向上改变模型参数,模型的行为会发生显著变化),而在垂直于这个平面的参数空间中,大部分方向都是sloppy(“欠定”)的。</div></td></tr>
<tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l16" >第16行:</td>
<td colspan="2" class="diff-lineno">第17行:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>为了表示及说明方便,取参数空间的一个二维平面截面来观察等值线,可得到形如下图香蕉形状的的成本函数等值线图。这个图的水平方向与垂直方向是按照sloppy(“欠定 ”)方向与stiff(“僵硬”)方向布置的。沿着sloppy(“欠定”)方向,成本函数变化小,而沿着stiff(“僵硬”)方向,成本函数变化大。</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>为了表示及说明方便,取参数空间的一个二维平面截面来观察等值线,可得到形如下图香蕉形状的的成本函数等值线图。这个图的水平方向与垂直方向是按照sloppy(“欠定 ”)方向与stiff(“僵硬”)方向布置的。沿着sloppy(“欠定”)方向,成本函数变化小,而沿着stiff(“僵硬”)方向,成本函数变化大。</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>模型与实际值符合最好的参数值会使成本函数取到极值,从这个参数值局部来看,成本函数的等值线呈现为椭球形,取成本函数的黑塞矩阵<math>H_{\alpha\beta} =\partial^2C/\partial\theta_\alpha\partial\theta_\beta</math><del class="diffchange diffchange-inline">。计算矩阵的特征值以及对应的特征向量,较大的特征值对应的特征向量方向即是stiff(“僵硬”)的。因此,特征值的平方(为了避免特征值是负的时,绝对值大但本身值很小的情况出现)即可以反应参数变化方向是stiff(“僵硬”)的还是sloppy(“欠定”)的。</del></div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>模型与实际值符合最好的参数值会使成本函数取到极值,从这个参数值局部来看,成本函数的等值线呈现为椭球形,取成本函数的黑塞矩阵<math>H_{\alpha\beta} =\partial^2C/\partial\theta_\alpha\partial\theta_\beta</math><ins class="diffchange diffchange-inline">。计算矩阵的特征值以及对应的特征向量,较大的特征值对应的特征向量方向即是stiff(“僵硬”)的。因此,特征值的平方(为了避免特征值是负的时,绝对值大但本身值很小的情况出现)即可以反映参数变化方向是stiff(“僵硬”)的还是sloppy(“欠定”)的。</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Sloppiness在生物学领域最为普遍,但在其它领域也并不缺席。从昆虫飞行模型,到原子间势,再到加速器设计,许多目前常用的模型都是sloppy的。例如,量子蒙特卡洛是求解原子和小分子的能量和量子行为的最精确的工具;然而,赛勒斯·乌姆里加(Cyrus Umrigar)在这种方法基础上建立的非常精确的变分波函数却是极度sloppy(b列)。</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Sloppiness在生物学领域最为普遍,但在其它领域也并不缺席。从昆虫飞行模型,到原子间势,再到加速器设计,许多目前常用的模型都是sloppy的。例如,量子蒙特卡洛是求解原子和小分子的能量和量子行为的最精确的工具;然而,赛勒斯·乌姆里加(Cyrus Umrigar)在这种方法基础上建立的非常精确的变分波函数却是极度sloppy(b列)。</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">即便参数值与真实值相差很大,有sloppy特性的模型也可以做出精确的预测。在数学中有一个经典的拟合难题:用指数衰变和去拟合放射性模型(c列和d列)得到的衰变常数与真实衰变常数截然不同,但短期内模型预测值与真实值却相差不大 </del>。最后,用多项式系数模型<math>\sum_i a_it^i</math>拟合数据是sloppy的(h列)。但用正交多项式基<math>\sum_ib_iH_i</math>(<math>H_i</math>是一组正交多项式基)去拟合时得到的模型却往往是非sloppy的,这是因为从<math>t^i</math>到<math>H_i</math><del class="diffchange diffchange-inline">的变换是高度非正交的。</del></div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">即便参数值与真实值相差很大,有sloppy特性的模型也可以做出精确的预测。在数学中有一个经典的拟合难题<ref name = “Waterfall et.al, 2006”>[https://sethna.lassp.cornell.edu/pubPDF/Vandermonde.pdf "Sloppy model universality class and the Vandermonde matrix"], Joshua J. Waterfall, Fergal P. Casey, Ryan N. Gutenkunst, Kevin S. Brown, Christopher R. Myers, Piet W. Brouwer, Veit Elser, and James P. Sethna, Phys. Rev. Letters '''97''', 150601 (2006), also selected for Virtual Journal of Biological Physics Research '''12 (8, Miscellaneous)''',(2006). </ref><ref name="Transtrum et.al, 2014">” Model reduction by manifold boundaries", Mark K. Transtrum, P. Qiu [https://doi.org/10.1103/PhysRevLett.113.098701 Phys. Rev. Lett. '''113''', 098701 (2014)]; pdf. </ref>:用指数衰变和去拟合放射性模型(c列和d列)得到的衰变常数与真实衰变常数截然不同,但短期内模型预测值与真实值却相差不大 </ins>。最后,用多项式系数模型<math>\sum_i a_it^i</math>拟合数据是sloppy的(h列)。但用正交多项式基<math>\sum_ib_iH_i</math>(<math>H_i</math>是一组正交多项式基)去拟合时得到的模型却往往是非sloppy的,这是因为从<math>t^i</math>到<math>H_i</math><ins class="diffchange diffchange-inline">的变换是高度非正交的<ref name="Transtrum et.al, 2014"/>。</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">Sloppy模型有着多种形式,每个模型的sloppniess的原因并不完全相同,部分系统的sloppiness的原因可以从数学上进行分析。但是不同系统的sloppiniess具体原因仍然极具复杂性。</del></div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">Sloppy模型有着多种形式,每个模型的sloppniess的原因并不完全相同,部分系统的sloppiness的原因可以从数学上进行分析。但是不同系统的sloppiniess具体原因仍然极具复杂性<ref> [https://arxiv.org/abs/1605.08705 Bridging Mechanistic and Phenomenological Models of Complex Biological Systems], Mark K. Transtrum and Peng Qiu, PLoS Comput Biol 12(5): e1004915. https://doi.org/10.1371/journal.pcbi.1004915</ref>。</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>==Sloppy 理论与物理学==</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>==Sloppy 理论与物理学==</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>事实上,科学能够向前发展与sloppy模型的普适性相连,任何一个系统都是由大量参数决定的,而人们能够发现系统的规律是因为系统的规律由少数stiff(“僵硬”)参数决定,而与大量的sloppy(“欠定”)参数无关。以声音传播的现象为例,声音传播与分子的大小、分子的速度等众多参数相关,但是要准确预测声音传播的速度只需要知道宏观的密度与压缩比。同样,高能物理学家不需要求解弦理论来预测希格斯玻色子或描述夸克的行为。</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>事实上,科学能够向前发展与sloppy模型的普适性相连,任何一个系统都是由大量参数决定的,而人们能够发现系统的规律是因为系统的规律由少数stiff(“僵硬”)参数决定,而与大量的sloppy(“欠定”)参数无关。以声音传播的现象为例,声音传播与分子的大小、分子的速度等众多参数相关,但是要准确预测声音传播的速度只需要知道宏观的密度与压缩比。同样,高能物理学家不需要求解弦理论来预测希格斯玻色子或描述夸克的行为。</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">事实上,理论物理学就像一棵树(下图)。高能物理学家研究树的枝条,寻找更接近树干的更统一的理论。在凝聚态物理学中则向外构建,寻找“涌现的”树枝和树叶——描述声音、半导体和超流体的有效理论。但两者有许多相似之处:扩散方程描述了在静止空气中香水如何从皮肤扩散到鼻子。这个方程通常写成连续极限的形式,使用的方法类似于描述凝聚态物理学中许多其他现象——声音、磁铁和超导体——的方法。而磁性的伊辛模型分形过程,通常使用类似于高能物理学中使用的重整化群进行分析。物理学家有一套系统的方法判断哪些参数是stiff(“僵硬”)的,哪些参数是sloppy(“欠定”)的,但是在其它领域中并没有相应的方法,使用sloppy理论的概念可以更准确有效地分析系统。</del></div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">事实上,理论物理学就像一棵树(下图)。高能物理学家研究树的枝条,寻找更接近树干的更统一的理论。在凝聚态物理学中则向外构建,寻找“涌现的”树枝和树叶——描述声音、半导体和超流体的有效理论。但两者有许多相似之处:扩散方程描述了在静止空气中香水如何从皮肤扩散到鼻子。这个方程通常写成连续极限的形式,使用的方法类似于描述凝聚态物理学中许多其他现象——声音、磁铁和超导体——的方法。而磁性的伊辛模型分形过程,通常使用类似于高能物理学中使用的重整化群进行分析。物理学家有一套系统的方法判断哪些参数是stiff(“僵硬”)的,哪些参数是sloppy(“欠定”)的,但是在其它领域中并没有相应的方法,使用sloppy理论的概念可以更准确有效地分析系统<ref>[http://arxiv.org/abs/1710.05787 Information geometry and the renormalization group], Archishman Raju, Benjamin B. Machta, James P. Sethna (submitted). </ref><ref>[http://arxiv.org/abs/1303.6738 Parameter Space Compression Underlies Emergent Theories and Predictive Models,] Benjamin B. Machta, Ricky Chachra, Mark K. Transtrum, James P. Sethna, [http://www.sciencemag.org/content/342/6158/604 Science'''342''' 604-607 (2013)].</ref><ref>[http://arxiv.org/pdf/1409.6203v2.pdf Information topology identifies emergent model classes], Transtrum M.K., Hart G., Qiu P. </ref><ref>[https://sethna.lassp.cornell.edu/pubPDF/vanderPol.pdf Structural susceptibility and separation of time scales in the van der Pol Oscillator], Ricky Chachra, Mark K. Transtrum, and James P. Sethna, [http://link.aps.org/doi/10.1103/PhysRevE.86.026712 Phys. Rev. E '''86''', 026712 (2012)]. </ref></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">,甚至物理学领域也在逐渐应用sloppy理论<ref name = “Quinn”>Model manifolds for probabilistic models: [http://arxiv.org/abs/1709.02000 Visualizing theory space: Isometric embedding of probabilistic predictions, from the Ising model to the cosmic microwave background], Katherine N. Quinn, Francesco De Bernardis, Michael D. Niemack, James P. Sethna (submitted). </ref><ref>[https://sethna.lassp.cornell.edu/pubPDF/SloppyEverywhere.pdf "Universally Sloppy Parameter Sensitivities in Systems Biology"], Ryan N. Gutenkunst, Joshua J. Waterfall, Fergal P. Casey, Kevin S. Brown, Christopher R. Myers, James P. Sethna, PLoS Comput Biol3(10) e189 (2007). ([http://compbiol.plosjournals.org/perlserv/?request=get-document&doi=10.1371/journal.pcbi.0030189 PLoS], [http://dx.doi.org/10.1371/journal.pcbi.0030189 doi:10.1371/journal.pcbi.0030189]). [Reviewed in [http://biomedicalcomputationreview.org/4/1/4.pdf NewsBytes] of [http://biomedicalcomputationreview.org/ Biomedical Computation Review] (Winter 07/08); rated "Exceptional" on Faculty of 1000]. </ref><ref name = “Casey et.al, 2007”>[http://arxiv.org/abs/q-bio.MN/0610024 "Optimal experimental design in an EGFR signaling and down-regulation model"], Fergal P. Casey, Dan Baird, Qiyu Feng, Ryan N. Gutenkunst, Joshua J. Waterfall, Christopher R. Myers, Kevin S. Brown, Richard A. Cerione, and James P. Sethna, IET Systems Biology 1, 190-202 (2007)</ref>。</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Sloppy理论对实验的启示及应用===</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>===Sloppy理论对实验的启示及应用===</div></td></tr>
<tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l36" >第36行:</td>
<td colspan="2" class="diff-lineno">第38行:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>但这并不是说可以忽略一些参数,哪怕忽略一个stiff(“僵硬”)方向有投影的参数,模型的行为也会完全不可控。这也给实验造成了困扰,有时忽略了一半以上的参数模型的行为不会有太大改变,但即使忽略了在stiff(“僵硬”)方向有投影的一个参数,要准确预测模型的行为也会变得完全不可能。</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>但这并不是说可以忽略一些参数,哪怕忽略一个stiff(“僵硬”)方向有投影的参数,模型的行为也会完全不可控。这也给实验造成了困扰,有时忽略了一半以上的参数模型的行为不会有太大改变,但即使忽略了在stiff(“僵硬”)方向有投影的一个参数,要准确预测模型的行为也会变得完全不可能。</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{| class="wikitable"</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{| class="wikitable"</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>| colspan="1" rowspan="1" |<del class="diffchange diffchange-inline">还是以上述48参数的模型为例,研究其在另一种工作模式下的行为:特定细胞在特定生长激素 </del>(EGF作用下)的活性 Erk 与时间的关系。如果 Erk 在 10 分钟后下降,细胞就会增殖;如果它保持下去,细胞就会分化(像神经元一样生长分支)。药物LY,红色X)会关闭两种蛋白质(图中左侧灰色的两个蛋白)。实验想确定给细胞提供更多的LY会发生什么。下面使用四种类型的预测。</div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>| colspan="1" rowspan="1" |<ins class="diffchange diffchange-inline">还是以上述48参数的模型为例<ref name = “Quinn”/>,研究其在另一种工作模式下的行为:特定细胞在特定生长激素 </ins>(EGF作用下)的活性 Erk 与时间的关系。如果 Erk 在 10 分钟后下降,细胞就会增殖;如果它保持下去,细胞就会分化(像神经元一样生长分支)。药物LY,红色X)会关闭两种蛋白质(图中左侧灰色的两个蛋白)。实验想确定给细胞提供更多的LY会发生什么。下面使用四种类型的预测。</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>#经验:一名生物学家认为图中左侧的回路在十分钟后会阻止Erk流失。因此,在添加药物LY后,他预测Erk将保持活跃。</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>#经验:一名生物学家认为图中左侧的回路在十分钟后会阻止Erk流失。因此,在添加药物LY后,他预测Erk将保持活跃。</div></td></tr>
<tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l60" >第60行:</td>
<td colspan="2" class="diff-lineno">第62行:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>这两个实验表明,在实验中可以减少测量参数的个数,并且通过选取测量的参数使实验更加有效。但是在许多情况下仍有很多问题。这里的模型是准确的,误差估计也很准确。这是建立大量已有实验的基础上,但对于未知的领域,规律往往并不清楚,虽然系统极有可能是sloppy的,但是正是因为有那些stiff(“僵硬”)的参数方向,仍然需要繁琐得测量所有参数。</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>这两个实验表明,在实验中可以减少测量参数的个数,并且通过选取测量的参数使实验更加有效。但是在许多情况下仍有很多问题。这里的模型是准确的,误差估计也很准确。这是建立大量已有实验的基础上,但对于未知的领域,规律往往并不清楚,虽然系统极有可能是sloppy的,但是正是因为有那些stiff(“僵硬”)的参数方向,仍然需要繁琐得测量所有参数。</div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>以上内容翻译自参考资料:</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>以上内容翻译自参考资料:</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">Sloopy </del>Model: <nowiki>https://www.lassp.cornell.edu/sethna/Sloppy/,感兴趣的朋友可以前往了解详情。</nowiki></div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">Sloppy </ins>Model: <nowiki>https://www.lassp.cornell.edu/sethna/Sloppy/,感兴趣的朋友可以前往了解详情。</nowiki></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>==参考资料==</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>==参考资料==</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{{Reflist}}</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{{Reflist}}</div></td></tr>
<!-- diff cache key wiki:diff::1.12:old-34328:rev-34376 -->
</table>
Leshell0609
https://wiki.swarma.org/index.php?title=%E7%9F%AD%E6%9C%9F%E7%AA%81%E8%A7%A6%E5%8F%AF%E5%A1%91%E6%80%A7&diff=34350&oldid=34340
短期突触可塑性
2024-03-05T05:00:48Z
<p></p>
<table class="diff diff-contentalign-left diff-editfont-monospace" data-mw="interface">
<col class="diff-marker" />
<col class="diff-content" />
<col class="diff-marker" />
<col class="diff-content" />
<tr class="diff-title" lang="zh-Hans-CN">
<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">←上一版本</td>
<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">2024年3月5日 (二) 05:00的版本</td>
</tr><tr><td colspan="4" class="diff-multi" lang="zh-Hans-CN">(未显示同一用户的5个中间版本)</td></tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l25" >第25行:</td>
<td colspan="2" class="diff-lineno">第25行:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><math>u</math>和<math>x</math>的动态之间的相互作用决定了<math>ux</math>的联合效应是由抑制还是促进所主导。在<math>\tau_d\gg \tau_f</math>和大<math>U</math>的参数区域,一个初始动作电位会导致<math>x</math>大幅下降,需要很长时间恢复;因此,突触是以STD为主(图1B)。在<math>\tau_f \gg \tau_d</math>和小<math>U</math>的参数区域,突触效能会逐渐通过动作电位增加,因此突触以STF为主(图1C)。这个现象学模型成功地再现了在许多皮层区域观察到的抑制和促进突触的动力学。</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><math>u</math>和<math>x</math>的动态之间的相互作用决定了<math>ux</math>的联合效应是由抑制还是促进所主导。在<math>\tau_d\gg \tau_f</math>和大<math>U</math>的参数区域,一个初始动作电位会导致<math>x</math>大幅下降,需要很长时间恢复;因此,突触是以STD为主(图1B)。在<math>\tau_f \gg \tau_d</math>和小<math>U</math>的参数区域,突触效能会逐渐通过动作电位增加,因此突触以STF为主(图1C)。这个现象学模型成功地再现了在许多皮层区域观察到的抑制和促进突触的动力学。</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">[[http://www.scholarpedia.org/article/File:Fig1A_short_term_plasticity.png Image:图1A]]<br /></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">[[http://www.scholarpedia.org/article/File:Fig1B_short_term_plasticity.png Image:图1B]]<br /></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">[[http://www.scholarpedia.org/article/File:Fig1C_short_term_plasticity.png Image:图1C]]<br /></ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">[[Image:Fig1A_short_term_plasticity.png|400px|链接=Special:FilePath/Fig1A_short_term_plasticity.png]]</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">[[Image:Fig1B_short_term_plasticity.png|350px|链接=Special:FilePath/Fig1B_short_term_plasticity.png]]</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">[[Image:Fig1C_short_term_plasticity.png|350px|链接=Special:FilePath/Fig1C_short_term_plasticity.png]] <br /></del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>图1. (A) 由Eqs. ({{EquationNote|1}}) 和({{EquationNote|2}}) 给出的STP的现象学模型。 (B) 由STD主导的突触产生的突触后电流。神经元发射率<math>R=15</math>Hz。参数<math>A=1</math>,<math>U=0.45</math>,<math>\tau_s=20</math>ms,<math>\tau_d=750</math>ms,和<math>\tau_f=50</math>ms。 (C) STF主导的突触的动态。参数<math>U=0.15</math>,<math>\tau_f=750</math>ms,和<math>\tau_d=50</math>ms。</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>图1. (A) 由Eqs. ({{EquationNote|1}}) 和({{EquationNote|2}}) 给出的STP的现象学模型。 (B) 由STD主导的突触产生的突触后电流。神经元发射率<math>R=15</math>Hz。参数<math>A=1</math>,<math>U=0.45</math>,<math>\tau_s=20</math>ms,<math>\tau_d=750</math>ms,和<math>\tau_f=50</math>ms。 (C) STF主导的突触的动态。参数<math>U=0.15</math>,<math>\tau_f=750</math>ms,和<math>\tau_d=50</math>ms。</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l54" >第54行:</td>
<td colspan="2" class="diff-lineno">第54行:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=== 时间过滤 ===</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=== 时间过滤 ===</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>上述分析仅描述了具有稳态发射率的神经群体发射。当前突触群体发射率随时间任意变化时,可以使用Eq. ({{EquationNote|3}}) <del class="diffchange diffchange-inline">来推导动态突触的过滤特性。在[[#附录A:短期抑制的时间过滤器的推导|附录A]]中,我们为以抑制为主的突触(</del><math>u^+ \approx U</math>)提出了相应的计算。考虑围绕恒定率<math>R_0>0</math>的小幅度扰动<math>R(t):=R_0 + R_1 \rho (t)</math>,其中<math>R_1\ll R_0</math>,突触电流<math>I</math>的傅立叶变换可以近似为</div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>上述分析仅描述了具有稳态发射率的神经群体发射。当前突触群体发射率随时间任意变化时,可以使用Eq. ({{EquationNote|3}}) <ins class="diffchange diffchange-inline">来推导动态突触的过滤特性。在附录A中,我们为以抑制为主的突触(</ins><math>u^+ \approx U</math>)提出了相应的计算。考虑围绕恒定率<math>R_0>0</math>的小幅度扰动<math>R(t):=R_0 + R_1 \rho (t)</math>,其中<math>R_1\ll R_0</math>,突触电流<math>I</math>的傅立叶变换可以近似为</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{{NumBlk|::|<math></div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{{NumBlk|::|<math></div></td></tr>
<tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l81" >第81行:</td>
<td colspan="2" class="diff-lineno">第81行:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>由于STD以频率依赖的方式抑制突触效能,因此有人提出STD提供了一种自动的增益控制机制,即通过为缓慢发射的传入纤维分配高增益,而为快速发射的传入纤维分配低增益([[#Abbott97|Abbott 97]], [[#Abbott04|Abbott 04]], [[#Cook03|Cook 03]])。如果稳定的前突触发射率<math>R</math>突然变化了一个量<math>\Delta R</math>,则在突触进一步被抑制之前,新率下的第一个动作电位将以效能<math>E</math>被传输。因此,突触输入的瞬时增加将与<math>\Delta R E(R)</math>成正比,对于大的发射率,这大约与<math>\Delta R/R</math>成正比(见上文)。这让人想起韦伯定律,该定律指出瞬时突触响应大致与输入发射率的百分比变化成正比。图2D显示,对于固定大小的率变化<math>\Delta R</math>,响应随着稳定输入值的增加而减少;而在没有STD的情况下,对于固定大小的率变化,响应将保持不变。</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>由于STD以频率依赖的方式抑制突触效能,因此有人提出STD提供了一种自动的增益控制机制,即通过为缓慢发射的传入纤维分配高增益,而为快速发射的传入纤维分配低增益([[#Abbott97|Abbott 97]], [[#Abbott04|Abbott 04]], [[#Cook03|Cook 03]])。如果稳定的前突触发射率<math>R</math>突然变化了一个量<math>\Delta R</math>,则在突触进一步被抑制之前,新率下的第一个动作电位将以效能<math>E</math>被传输。因此,突触输入的瞬时增加将与<math>\Delta R E(R)</math>成正比,对于大的发射率,这大约与<math>\Delta R/R</math>成正比(见上文)。这让人想起韦伯定律,该定律指出瞬时突触响应大致与输入发射率的百分比变化成正比。图2D显示,对于固定大小的率变化<math>\Delta R</math>,响应随着稳定输入值的增加而减少;而在没有STD的情况下,对于固定大小的率变化,响应将保持不变。</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[[<del class="diffchange diffchange-inline">Image</del>:<del class="diffchange diffchange-inline">Fig2A_short_term_plasticity</del>.<del class="diffchange diffchange-inline">png|300px|链接=Special</del>:<del class="diffchange diffchange-inline">FilePath/</del>Fig2A_short_term_plasticity.png]] </div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[[<ins class="diffchange diffchange-inline">http</ins>:<ins class="diffchange diffchange-inline">//www.scholarpedia</ins>.<ins class="diffchange diffchange-inline">org/article/File</ins>:Fig2A_short_term_plasticity.png <ins class="diffchange diffchange-inline">Image:图2A</ins>]]<ins class="diffchange diffchange-inline"><br /></ins></div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[[<del class="diffchange diffchange-inline">Image</del>:<del class="diffchange diffchange-inline">Fig2B_short_term_plasticity</del>.<del class="diffchange diffchange-inline">png|300px|链接=Special</del>:<del class="diffchange diffchange-inline">FilePath/</del>Fig2B_short_term_plasticity.png]] </div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[[<ins class="diffchange diffchange-inline">http</ins>:<ins class="diffchange diffchange-inline">//www.scholarpedia</ins>.<ins class="diffchange diffchange-inline">org/article/File</ins>:Fig2B_short_term_plasticity.png <ins class="diffchange diffchange-inline">Image:图2B</ins>]]<ins class="diffchange diffchange-inline"><br /></ins></div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[[<del class="diffchange diffchange-inline">Image</del>:<del class="diffchange diffchange-inline">Fig2C_short_term_plasticity</del>.<del class="diffchange diffchange-inline">png|300px|链接=Special</del>:<del class="diffchange diffchange-inline">FilePath/</del>Fig2C_short_term_plasticity.png]] </div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[[<ins class="diffchange diffchange-inline">http</ins>:<ins class="diffchange diffchange-inline">//www.scholarpedia</ins>.<ins class="diffchange diffchange-inline">org/article/File</ins>:Fig2C_short_term_plasticity.png <ins class="diffchange diffchange-inline">Image:图2C</ins>]]<ins class="diffchange diffchange-inline"><br /></ins></div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[[<del class="diffchange diffchange-inline">Image</del>:<del class="diffchange diffchange-inline">Fig2D_short_term_plasticity</del>.<del class="diffchange diffchange-inline">png|300px|链接=Special</del>:<del class="diffchange diffchange-inline">FilePath/</del>Fig2D_short_term_plasticity.png]] <br /></div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[[<ins class="diffchange diffchange-inline">http</ins>:<ins class="diffchange diffchange-inline">//www.scholarpedia</ins>.<ins class="diffchange diffchange-inline">org/article/File</ins>:Fig2D_short_term_plasticity.png <ins class="diffchange diffchange-inline">Image:图2D</ins>]]<br /></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>图2. (A) 以STD为主的突触的效能稳态值以及它产生的突触后电流,分别由<math>ux</math>和<math>uxR</math>测量。参数与图1B相同。 (B) 以STF为主的突触的同上。参数与图1C相同。 (C) 以STD为主的突触的过滤特性,由<math>|\widehat{\chi}(w)|</math> [Eq. ({{EquationNote|6}})]测量。 (D) 突触对突然输入变化<math>\Delta R</math>的神经响应与稳定率值的关系,对于STD占主导的突触。<math>\Delta R=5</math>Hz。参数与图1B相同。</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>图2. (A) 以STD为主的突触的效能稳态值以及它产生的突触后电流,分别由<math>ux</math>和<math>uxR</math>测量。参数与图1B相同。 (B) 以STF为主的突触的同上。参数与图1C相同。 (C) 以STD为主的突触的过滤特性,由<math>|\widehat{\chi}(w)|</math> [Eq. ({{EquationNote|6}})]测量。 (D) 突触对突然输入变化<math>\Delta R</math>的神经响应与稳定率值的关系,对于STD占主导的突触。<math>\Delta R=5</math>Hz。参数与图1B相同。</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l113" >第113行:</td>
<td colspan="2" class="diff-lineno">第114行:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>包含STP后,CANN显示出新的有趣动态行为。其中之一是自发的行波现象([[#York09|York 09]],[[#Fung12a|Fung 12]],[[#Bressloff12|Bressloff 12]])(图3C)。考虑一个最初处于局部化“bump”状态的网络。由于STD,"bump"区域内的神经互动被削弱。由于来自邻近吸引子状态的竞争,一个小的位移将推动“bump”离开,并且由于STD效应,它将继续朝那个方向移动。如果网络受到连续移动输入的驱动,在适当的参数范围内,“bump”的移动甚至可以不管输入移动速度如何,始终领先外部驱动一定时间,实现一种预测行为,这让人联想到啮齿类动物头部方向神经元的预测响应(图3D;[[#Fung12b|Fung 12]])。</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>包含STP后,CANN显示出新的有趣动态行为。其中之一是自发的行波现象([[#York09|York 09]],[[#Fung12a|Fung 12]],[[#Bressloff12|Bressloff 12]])(图3C)。考虑一个最初处于局部化“bump”状态的网络。由于STD,"bump"区域内的神经互动被削弱。由于来自邻近吸引子状态的竞争,一个小的位移将推动“bump”离开,并且由于STD效应,它将继续朝那个方向移动。如果网络受到连续移动输入的驱动,在适当的参数范围内,“bump”的移动甚至可以不管输入移动速度如何,始终领先外部驱动一定时间,实现一种预测行为,这让人联想到啮齿类动物头部方向神经元的预测响应(图3D;[[#Fung12b|Fung 12]])。</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[[<del class="diffchange diffchange-inline">Image</del>:<del class="diffchange diffchange-inline">Fig3AB_short_term_plasticity</del>.<del class="diffchange diffchange-inline">png|700px|链接=Special</del>:<del class="diffchange diffchange-inline">FilePath/</del>Fig3AB_short_term_plasticity.png]] </div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[[<ins class="diffchange diffchange-inline">http</ins>:<ins class="diffchange diffchange-inline">//www.scholarpedia</ins>.<ins class="diffchange diffchange-inline">org/article/File</ins>:Fig3AB_short_term_plasticity.png <ins class="diffchange diffchange-inline">Image:图3AB</ins>]]<ins class="diffchange diffchange-inline"><br /></ins></div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[[<del class="diffchange diffchange-inline">Image</del>:<del class="diffchange diffchange-inline">Fig3C-TravellingWave</del>.<del class="diffchange diffchange-inline">gif|链接=Special</del>:<del class="diffchange diffchange-inline">FilePath/</del>Fig3C-TravellingWave.gif]] </div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[[<ins class="diffchange diffchange-inline">http</ins>:<ins class="diffchange diffchange-inline">//www.scholarpedia</ins>.<ins class="diffchange diffchange-inline">org/article/File</ins>:Fig3C-TravellingWave.gif <ins class="diffchange diffchange-inline">Image:图3C</ins>]]<ins class="diffchange diffchange-inline"><br /></ins></div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[[<del class="diffchange diffchange-inline">Image</del>:<del class="diffchange diffchange-inline">Fig3D-Leading</del>.<del class="diffchange diffchange-inline">gif|链接=Special</del>:<del class="diffchange diffchange-inline">FilePath/</del>Fig3D-Leading.gif]] <br /></div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[[<ins class="diffchange diffchange-inline">http</ins>:<ins class="diffchange diffchange-inline">//www.scholarpedia</ins>.<ins class="diffchange diffchange-inline">org/article/File</ins>:Fig3D-Leading.gif <ins class="diffchange diffchange-inline">Image:图3D</ins>]]<br /></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>图3. (A,B) 对外部兴奋脉冲响应产生的STD主导网络的群体尖峰。当脉冲的呈现率较低时(A),网络对它们中的每一个都做出响应。对于更高的呈现率(B),网络只对一部分输入做出响应。改编自([[#Loebel02|Loebel 02]])。(C) CANN中STD产生的行波。(D) 包含STD的CANN的预测性跟踪行为。</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>图3. (A,B) 对外部兴奋脉冲响应产生的STD主导网络的群体尖峰。当脉冲的呈现率较低时(A),网络对它们中的每一个都做出响应。对于更高的呈现率(B),网络只对一部分输入做出响应。改编自([[#Loebel02|Loebel 02]])。(C) CANN中STD产生的行波。(D) 包含STD的CANN的预测性跟踪行为。</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<!-- diff cache key wiki:diff::1.12:old-34340:rev-34350 -->
</table>
Dongyu
https://wiki.swarma.org/index.php?title=%E7%94%A8%E6%88%B7:Dongyu&diff=34344&oldid=34289
用户:Dongyu
2024-03-05T04:31:51Z
<p><span dir="auto"><span class="autocomment">Personal Website</span></span></p>
<table class="diff diff-contentalign-left diff-editfont-monospace" data-mw="interface">
<col class="diff-marker" />
<col class="diff-content" />
<col class="diff-marker" />
<col class="diff-content" />
<tr class="diff-title" lang="zh-Hans-CN">
<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">←上一版本</td>
<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">2024年3月5日 (二) 04:31的版本</td>
</tr><tr><td colspan="4" class="diff-multi" lang="zh-Hans-CN">(未显示同一用户的3个中间版本)</td></tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l10" >第10行:</td>
<td colspan="2" class="diff-lineno">第10行:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* How does intelligence emerge from biological and artificial systems?</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* How does intelligence emerge from biological and artificial systems?</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* How can neuroscience inspire the development of artificial general intelligence (AGI) and artificial super intelligence (ASI)?</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* How can neuroscience inspire the development of artificial general intelligence (AGI) and artificial super intelligence (ASI)?</div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">== Personal Website ==</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">[https://daniel-gong.github.io Website]</ins></div></td></tr>
<!-- diff cache key wiki:diff::1.12:old-34289:rev-34344 -->
</table>
Dongyu
https://wiki.swarma.org/index.php?title=%E7%9F%AD%E6%9C%9F%E7%AA%81%E8%A7%A6%E5%8F%AF%E5%A1%91%E6%80%A7&diff=34340&oldid=34332
短期突触可塑性
2024-03-05T04:29:11Z
<p>译者注</p>
<table class="diff diff-contentalign-left diff-editfont-monospace" data-mw="interface">
<col class="diff-marker" />
<col class="diff-content" />
<col class="diff-marker" />
<col class="diff-content" />
<tr class="diff-title" lang="zh-Hans-CN">
<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">←上一版本</td>
<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">2024年3月5日 (二) 04:29的版本</td>
</tr><tr><td colspan="4" class="diff-multi" lang="zh-Hans-CN">(未显示同一用户的1个中间版本)</td></tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1" >第1行:</td>
<td colspan="2" class="diff-lineno">第1行:</td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>'''短期突触可塑性'''('''Short-term synaptic plasticity''','''STP''')<del class="diffchange diffchange-inline"><ref></del>[[#Stevens95|Stevens 95]], [[#Markram96|Markram 96]], [[#Abbott97|Abbott 97]], [[#Zucker02|Zucker 02]], [[#Abbott04|Abbott 04]]<del class="diffchange diffchange-inline"></ref></del>,也称为动态突触,指的是突触效能随时间变化的现象,这种变化反映了前突触活动的历史。在实验中观察到两种具有相反效果的STP,它们被称为短期抑制('''Short-Term Depression''', '''STD''')和短期促进('''Short-Term Facilitation''', '''STF'''<del class="diffchange diffchange-inline">)。STD是由于在突触信号传递过程中消耗的神经递质在前突触神经元的轴突末端耗尽所致,而STF是由于在产生动作电位后钙离子流入轴突末端,增加了神经递质释放的概率。STP在各种皮层区域中都有发现,并且在属性上展现出巨大的多样性。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,或是两种形式的混合。</del></div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>'''短期突触可塑性'''('''Short-term synaptic plasticity''','''STP''')<ins class="diffchange diffchange-inline">(</ins>[[#Stevens95|Stevens 95]], [[#Markram96|Markram 96]], [[#Abbott97|Abbott 97]], [[#Zucker02|Zucker 02]], [[#Abbott04|Abbott 04]]<ins class="diffchange diffchange-inline">)</ins>,也称为动态突触,指的是突触效能随时间变化的现象,这种变化反映了前突触活动的历史。在实验中观察到两种具有相反效果的STP,它们被称为短期抑制('''Short-Term Depression''', '''STD''')和短期促进('''Short-Term Facilitation''', '''STF'''<ins class="diffchange diffchange-inline">)。STD是由于在突触信号传递过程中消耗的神经递质在前突触神经元的轴突末端耗尽所致,而STF是由于在产生动作电位后钙离子流入轴突末端,增加了神经递质释放的概率。STP在各种皮层区域中都有发现,并且在属性上展现出巨大的多样性([[#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,或是两种形式的混合。</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">与长期可塑性相比,后者被假设为体验依赖性神经回路修改的神经基质,STP具有更短的时间尺度,通常在数百到数千毫秒的范围内。它对突触效能的修改是暂时的。如果没有持续的前突触活动,突触效能将迅速回到基线水平。</del></div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">与长期可塑性([[#Bi01|Bi 01]])相比,后者被假设为体验依赖性神经回路修改的神经基质,STP具有更短的时间尺度,通常在数百到数千毫秒的范围内。它对突触效能的修改是暂时的。如果没有持续的前突触活动,突触效能将迅速回到基线水平。</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">尽管STP似乎是突触生理学的不可避免后果,理论研究表明,其在大脑功能中的作用可能是深远的。从计算角度看,STP的时间尺度位于快速神经信号(以毫秒计)和经验诱导的学习(以分钟或更长时间计)之间。这是日常生活中许多过程发生的时间尺度,例如运动控制、语音识别和工作记忆。因此,STP很可能作为处理相关时间尺度上的时间信息的神经基质。STP意味着后突触神经元的反应依赖于前突触活动的历史,创造了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,赋予神经系统使用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计算功能的显著兴趣。</del></div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">尽管STP似乎是突触生理学的不可避免后果,理论研究表明,其在大脑功能中的作用可能是深远的(参见[[#ResearchTopic|Research Topic]]中的文献)。从计算角度看,STP的时间尺度位于快速神经信号(以毫秒计)和经验诱导的学习(以分钟或更长时间计)之间。这是日常生活中许多过程发生的时间尺度,例如运动控制、语音识别和工作记忆。因此,STP很可能作为处理相关时间尺度上的时间信息的神经基质。STP意味着后突触神经元的反应依赖于前突触活动的历史,创造了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,赋予神经系统使用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计算功能的显著兴趣。</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== 现象学模型 ==</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== 现象学模型 ==</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">STP背后的生物物理过程十分复杂。研究STP的计算角色依赖于简化的现象学模型的创建。</del></div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">STP背后的生物物理过程十分复杂。研究STP的计算角色依赖于简化的现象学模型的创建([[#Abbott97|Abbott 97]],[[#Markram98|Markram 98]],[[#Tsodyks98|Tsodyks 98]])。</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">在Tsodyks和Markram提出的模型中,STD效应通过一个归一化变量</del><math>x</math>(<math>0\leq x \leq1</math>)来模拟,表示在神经递质耗尽后仍然可用的资源比例。STF效应通过利用参数<math>u</math>来模拟,代表准备使用的可用资源比例(释放概率)。在发生动作电位后,(i)由于动作电位引起的钙离子流入前突触末端,<math>u</math>增加,之后(ii)一部分<math>u</math>的可用资源被消耗以产生突触后电流。在动作电位之间,<math>u</math>随时间常数<math>\tau_f</math>衰减回零,<math>x</math>随时间常数<math>\tau_d </math>恢复到1。总之,STP的动力学由下式给出</div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">在Tsodyks和Markram提出的模型([[#Tsodyks98|Tsodyks 98]])中,STD效应通过一个归一化变量</ins><math>x</math>(<math>0\leq x \leq1</math>)来模拟,表示在神经递质耗尽后仍然可用的资源比例。STF效应通过利用参数<math>u</math>来模拟,代表准备使用的可用资源比例(释放概率)。在发生动作电位后,(i)由于动作电位引起的钙离子流入前突触末端,<math>u</math>增加,之后(ii)一部分<math>u</math>的可用资源被消耗以产生突触后电流。在动作电位之间,<math>u</math>随时间常数<math>\tau_f</math>衰减回零,<math>x</math>随时间常数<math>\tau_d </math>恢复到1。总之,STP的动力学由下式给出</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{{NumBlk|::|<math>\begin{aligned}</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{{NumBlk|::|<math>\begin{aligned}</div></td></tr>
<tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l33" >第33行:</td>
<td colspan="2" class="diff-lineno">第33行:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== 对信息传输的影响 ==</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== 对信息传输的影响 ==</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">由于STP根据前突触活动的历史修改突触效能,它可以改变神经信息传输。一般来说,以STD为主的突触更倾向于在低发射率下促进信息传输,因为高频率的动作电位会迅速使突触失活。然而,以STF为主的突触倾向于优化高频爆发的信息传输,这会增加突触强度。</del></div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">由于STP根据前突触活动的历史修改突触效能,它可以改变神经信息传输([[#Abbott97|Abbott 97]], [[#Tsodyks97|Tsodyks 97]], [[#Fuhrmann02|Fuhrmann 02]], [[#Rotman11|Rotman 11]], [[#Rosenbaum12|Rosenbaum 12]])。一般来说,以STD为主的突触更倾向于在低发射率下促进信息传输,因为高频率的动作电位会迅速使突触失活。然而,以STF为主的突触倾向于优化高频爆发的信息传输,这会增加突触强度。</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>通过动态突触的发射率依赖传输可以通过检查从大量神经元群体传输不相关泊松动作电位列的信息来分析,这些神经元群体具有全局发射率<math>R(t)</math>。可以通过对应于不同动作电位列的泊松过程的不同实现平均Eq. ({{EquationNote|1}}) 来获得突触后电流<math>I(t)</math><del class="diffchange diffchange-inline">的时间演化:</del></div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>通过动态突触的发射率依赖传输可以通过检查从大量神经元群体传输不相关泊松动作电位列的信息来分析,这些神经元群体具有全局发射率<math>R(t)</math>。可以通过对应于不同动作电位列的泊松过程的不同实现平均Eq. ({{EquationNote|1}}) 来获得突触后电流<math>I(t)</math><ins class="diffchange diffchange-inline">的时间演化([[#Tsodyks98|Tsodyks 98]]):</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{{NumBlk|::|<math>\begin{aligned}</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{{NumBlk|::|<math>\begin{aligned}</div></td></tr>
<tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l54" >第54行:</td>
<td colspan="2" class="diff-lineno">第54行:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=== 时间过滤 ===</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=== 时间过滤 ===</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>上述分析仅描述了具有稳态发射率的神经群体发射。当前突触群体发射率随时间任意变化时,可以使用Eq. ({{EquationNote|3}}) 来推导动态突触的过滤特性。在[[#<del class="diffchange diffchange-inline">附录A:短期抑制的时间过滤器推导</del>|附录A]]中,我们为以抑制为主的突触(<math>u^+ \approx U</math>)提出了相应的计算。考虑围绕恒定率<math>R_0>0</math>的小幅度扰动<math>R(t):=R_0 + R_1 \rho (t)</math>,其中<math>R_1\ll R_0</math>,突触电流<math>I</math>的傅立叶变换可以近似为</div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>上述分析仅描述了具有稳态发射率的神经群体发射。当前突触群体发射率随时间任意变化时,可以使用Eq. ({{EquationNote|3}}) 来推导动态突触的过滤特性。在[[#<ins class="diffchange diffchange-inline">附录A:短期抑制的时间过滤器的推导</ins>|附录A]]中,我们为以抑制为主的突触(<math>u^+ \approx U</math>)提出了相应的计算。考虑围绕恒定率<math>R_0>0</math>的小幅度扰动<math>R(t):=R_0 + R_1 \rho (t)</math>,其中<math>R_1\ll R_0</math>,突触电流<math>I</math>的傅立叶变换可以近似为</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{{NumBlk|::|<math></div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{{NumBlk|::|<math></div></td></tr>
<tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l73" >第73行:</td>
<td colspan="2" class="diff-lineno">第73行:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>过滤器的幅度<math>|\widehat{\chi}(w)|</math>如图2C所示,展示了抑制性突触的高通过滤特性。换句话说,前突触发射率的快速变化可以忠实地传输给突触后目标,而慢速变化则被抑制所衰减。</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>过滤器的幅度<math>|\widehat{\chi}(w)|</math>如图2C所示,展示了抑制性突触的高通过滤特性。换句话说,前突触发射率的快速变化可以忠实地传输给突触后目标,而慢速变化则被抑制所衰减。</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">STP还可以通过其他方式调节信息传输。例如,STD可能有助于去除时间输入中的自相关性,因为时间上接近的动作电位倾向于放大抑制效应,从而降低突触后电位的输出相关性。另一方面,STF的效果被时间上接近的动作电位放大,提高了突触后神经元对时间相关输入的敏感性。</del></div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">STP还可以通过其他方式调节信息传输。例如,STD可能有助于去除时间输入中的自相关性,因为时间上接近的动作电位倾向于放大抑制效应,从而降低突触后电位的输出相关性([[#Goldman02|Goldman 02]])。另一方面,STF的效果被时间上接近的动作电位放大,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]])。</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">通过结合STD和STF,可以进一步改善神经信息传输。例如,通过结合以STF为主的兴奋性突触和以STD为主的抑制性突触,可以增强突触后神经元对高频时段的检测。在接收到以STD为主和以STF为主的输入的突触后神经元中,神经反应可以显示出低通和高通过滤特性。</del></div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">通过结合STD和STF,可以进一步改善神经信息传输。例如,通过结合以STF为主的兴奋性突触和以STD为主的抑制性突触,可以增强突触后神经元对高频时段的检测 ([[#Klyachko06|Klyachko 06]])。在接收到以STD为主和以STF为主的输入的突触后神经元中,神经反应可以显示出低通和高通过滤特性([[#Fortune01|Fortune 01]])。</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=== 增益控制 ===</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=== 增益控制 ===</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">由于STD以频率依赖的方式抑制突触效能,因此有人提出STD提供了一种自动的增益控制机制,即通过为缓慢发射的传入纤维分配高增益,而为快速发射的传入纤维分配低增益。如果稳定的前突触发射率</del><math>R</math>突然变化了一个量<math>\Delta R</math>,则在突触进一步被抑制之前,新率下的第一个动作电位将以效能<math>E</math>被传输。因此,突触输入的瞬时增加将与<math>\Delta R E(R)</math>成正比,对于大的发射率,这大约与<math>\Delta R/R</math>成正比(见上文)。这让人想起韦伯定律,该定律指出瞬时突触响应大致与输入发射率的百分比变化成正比。图2D显示,对于固定大小的率变化<math>\Delta R</math>,响应随着稳定输入值的增加而减少;而在没有STD的情况下,对于固定大小的率变化,响应将保持不变。</div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">由于STD以频率依赖的方式抑制突触效能,因此有人提出STD提供了一种自动的增益控制机制,即通过为缓慢发射的传入纤维分配高增益,而为快速发射的传入纤维分配低增益([[#Abbott97|Abbott 97]], [[#Abbott04|Abbott 04]], [[#Cook03|Cook 03]])。如果稳定的前突触发射率</ins><math>R</math>突然变化了一个量<math>\Delta R</math>,则在突触进一步被抑制之前,新率下的第一个动作电位将以效能<math>E</math>被传输。因此,突触输入的瞬时增加将与<math>\Delta R E(R)</math>成正比,对于大的发射率,这大约与<math>\Delta R/R</math>成正比(见上文)。这让人想起韦伯定律,该定律指出瞬时突触响应大致与输入发射率的百分比变化成正比。图2D显示,对于固定大小的率变化<math>\Delta R</math>,响应随着稳定输入值的增加而减少;而在没有STD的情况下,对于固定大小的率变化,响应将保持不变。</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Image:Fig2A_short_term_plasticity.png|300px|链接=Special:FilePath/Fig2A_short_term_plasticity.png]] </div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Image:Fig2A_short_term_plasticity.png|300px|链接=Special:FilePath/Fig2A_short_term_plasticity.png]] </div></td></tr>
<tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l242" >第242行:</td>
<td colspan="2" class="diff-lineno">第242行:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== 参考文献 ==</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== 参考文献 ==</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* <span id="ResearchTopic"/> '''Research Topic''': ''Neural Information Processing with Dynamical Synapses''. S. Wu, K. Y. Michael Wong and M. Tsodyks. ''Frontiers in Computational Neuroscience'', 2013 [<del class="diffchange diffchange-inline">http</del>://www.frontiersin.org/<del class="diffchange diffchange-inline">Computational_Neuroscience</del>/<del class="diffchange diffchange-inline">researchtopics</del>/<del class="diffchange diffchange-inline">Neural_Information_Processing_/821 </del>link]</div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* <span id="ResearchTopic"/> '''Research Topic''': ''Neural Information Processing with Dynamical Synapses''. S. Wu, K. Y. Michael Wong and M. Tsodyks. ''Frontiers in Computational Neuroscience'', 2013 [<ins class="diffchange diffchange-inline">https</ins>://www.frontiersin.org/<ins class="diffchange diffchange-inline">research-topics</ins>/<ins class="diffchange diffchange-inline">821</ins>/<ins class="diffchange diffchange-inline">neural-information-processing-with-dynamical-synapses </ins>link]</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*{{Bibitem article 1|Synaptic Depression and Cortical Gain Control.|Science.|275(5297)|1997|221-224|Abbott|L. F. et al|preprint=[http://dx.doi.org/10.1126/science.275.5297.221 doi:10.1126/science.275.5297.221]|label=Abbott97|doi=10.1126/science.275.5297.221}}</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*{{Bibitem article 1|Synaptic Depression and Cortical Gain Control.|Science.|275(5297)|1997|221-224|Abbott|L. F. et al|preprint=[http://dx.doi.org/10.1126/science.275.5297.221 doi:10.1126/science.275.5297.221]|label=Abbott97|doi=10.1126/science.275.5297.221}}</div></td></tr>
<tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l284" >第284行:</td>
<td colspan="2" class="diff-lineno">第284行:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*{{Bibitem article 2| Short-Term Synaptic Plasticity.|Annual Review of Physiology.|64(1)|2002|355-405|Zucker|Robert S.|Regehr|Wade G.|preprint=[http://dx.doi.org/10.1146/annurev.physiol.64.092501.114547 doi:10.1146/annurev.physiol.64.092501.114547]|label=Zucker02}}</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>*{{Bibitem article 2| Short-Term Synaptic Plasticity.|Annual Review of Physiology.|64(1)|2002|355-405|Zucker|Robert S.|Regehr|Wade G.|preprint=[http://dx.doi.org/10.1146/annurev.physiol.64.092501.114547 doi:10.1146/annurev.physiol.64.092501.114547]|label=Zucker02}}</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">[http</del>://www.scholarpedia.org/article/Short-term_synaptic_plasticity <del class="diffchange diffchange-inline">参考http</del>://<del class="diffchange diffchange-inline">www</del>.<del class="diffchange diffchange-inline">scholarpedia</del>.org/<del class="diffchange diffchange-inline">article</del>/<del class="diffchange diffchange-inline">Short-term_synaptic_plasticity</del>]</div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">== 译者注 ==</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">'''本文翻译自Scholarpedia:http</ins>://www.scholarpedia.org/article/Short-term_synaptic_plasticity<ins class="diffchange diffchange-inline"><nowiki/>,遵守 CC3.0协议。'''</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">'''译者:[https</ins>://<ins class="diffchange diffchange-inline">wiki</ins>.<ins class="diffchange diffchange-inline">swarma</ins>.org/<ins class="diffchange diffchange-inline">index.php</ins>/<ins class="diffchange diffchange-inline">%E7%94%A8%E6%88%B7:Dongyu Dongyu Gong]'''</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">[[分类:神经可塑性]]</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">[[分类:神经科学]</ins>]</div></td></tr>
<!-- diff cache key wiki:diff::1.12:old-34332:rev-34340 -->
</table>
Dongyu
https://wiki.swarma.org/index.php?title=%E6%A8%A1%E6%9D%BF:Bibitem_article_etal&diff=34338&oldid=0
模板:Bibitem article etal
2024-03-05T04:07:45Z
<p>建立内容为“<span id="{{{label}}}">{{{6|SURNAME1}}}, {{{7|FORENAME1}}} et al. ({{{4|YEAR}}}{{{letter|}}}).</span> {{{1|TITLE}}} <em>{{{2|JOURNAL}}}</em> {{{3|VOLUMEWITHNUMBER…”的新页面</p>
<p><b>新页面</b></p><div><span id="{{{label}}}">{{{6|SURNAME1}}}, {{{7|FORENAME1}}} et al. ({{{4|YEAR}}}{{{letter|}}}).</span> {{{1|TITLE}}} <em>{{{2|JOURNAL}}}</em> {{{3|VOLUMEWITHNUMBER}}}: {{{5|PAGES}}}. {{#if:{{{doi|}}}|[http://dx.doi.org/{{{doi|}}} doi:{{{doi|}}}]. |}}{{{preprint|}}}<noinclude><br />
{{Bibitem article N doc}}<br />
</noinclude></div>
Dongyu
https://wiki.swarma.org/index.php?title=%E6%A8%A1%E6%9D%BF:Bibitem_article_5&diff=34337&oldid=0
模板:Bibitem article 5
2024-03-05T04:07:26Z
<p>建立内容为“<span id="{{{label}}}">{{{6|SURNAME1}}}, {{{7|FORENAME1}}}; {{{8|SURNAME2}}}, {{{9|FORENAME2}}}; {{{10|SURNAME3}}}, {{{11|FORENAME3}}}; {{{12|SURNAME4}}}, {{{13|F…”的新页面</p>
<p><b>新页面</b></p><div><span id="{{{label}}}">{{{6|SURNAME1}}}, {{{7|FORENAME1}}}; {{{8|SURNAME2}}}, {{{9|FORENAME2}}}; {{{10|SURNAME3}}}, {{{11|FORENAME3}}}; {{{12|SURNAME4}}}, {{{13|FORENAME4}}} and {{{14|SURNAME5}}}, {{{15|FORENAME5}}} ({{{4|YEAR}}}{{{letter|}}}).</span> {{{1|TITLE}}} <em>{{{2|JOURNAL}}}</em> {{{3|VOLUMEWITHNUMBER}}}: {{{5|PAGES}}}. {{#if:{{{doi|}}}|[http://dx.doi.org/{{{doi|}}} doi:{{{doi|}}}]. |}}{{{preprint|}}}<noinclude><br />
{{Bibitem article N doc}}<br />
</noinclude></div>
Dongyu
https://wiki.swarma.org/index.php?title=%E6%A8%A1%E6%9D%BF:Bibitem_article_4&diff=34336&oldid=0
模板:Bibitem article 4
2024-03-05T04:07:13Z
<p>建立内容为“<span id="{{{label}}}">{{{6|SURNAME1}}}, {{{7|FORENAME1}}}; {{{8|SURNAME2}}}, {{{9|FORENAME2}}}; {{{10|SURNAME3}}}, {{{11|FORENAME3}}} and {{{12|SURNAME4}}}, {{{1…”的新页面</p>
<p><b>新页面</b></p><div><span id="{{{label}}}">{{{6|SURNAME1}}}, {{{7|FORENAME1}}}; {{{8|SURNAME2}}}, {{{9|FORENAME2}}}; {{{10|SURNAME3}}}, {{{11|FORENAME3}}} and {{{12|SURNAME4}}}, {{{13|FORENAME4}}} ({{{4|YEAR}}}{{{letter|}}}).</span> {{{1|TITLE}}} <em>{{{2|JOURNAL}}}</em> {{{3|VOLUMEWITHNUMBER}}}: {{{5|PAGES}}}. {{#if:{{{doi|}}}|[http://dx.doi.org/{{{doi|}}} doi:{{{doi|}}}]. |}}{{{preprint|}}}<noinclude><br />
{{Bibitem article N doc}}<br />
</noinclude></div>
Dongyu
https://wiki.swarma.org/index.php?title=%E6%A8%A1%E6%9D%BF:Bibitem_article_3&diff=34335&oldid=0
模板:Bibitem article 3
2024-03-05T04:07:02Z
<p>建立内容为“<span id="{{{label}}}">{{{6|SURNAME1}}}, {{{7|FORENAME1}}}; {{{8|SURNAME2}}}, {{{9|FORENAME2}}} and {{{10|SURNAME3}}}, {{{11|FORENAME3}}} ({{{4|YEAR}}}{{{letter|}…”的新页面</p>
<p><b>新页面</b></p><div><span id="{{{label}}}">{{{6|SURNAME1}}}, {{{7|FORENAME1}}}; {{{8|SURNAME2}}}, {{{9|FORENAME2}}} and {{{10|SURNAME3}}}, {{{11|FORENAME3}}} ({{{4|YEAR}}}{{{letter|}}}).</span> {{{1|TITLE}}} <em>{{{2|JOURNAL}}}</em> {{{3|VOLUMEWITHNUMBER}}}: {{{5|PAGES}}}. {{#if:{{{doi|}}}|[http://dx.doi.org/{{{doi|}}} doi:{{{doi|}}}]. |}}{{{preprint|}}}<noinclude><br />
{{Bibitem article N doc}}<br />
</noinclude></div>
Dongyu
https://wiki.swarma.org/index.php?title=%E6%A8%A1%E6%9D%BF:Bibitem_article_2&diff=34334&oldid=0
模板:Bibitem article 2
2024-03-05T04:06:45Z
<p>建立内容为“<span id="{{{label}}}">{{{6|SURNAME1}}}({{{4|YEAR}}}{{{letter|}}}).</span> {{{1|TITLE}}} <em>{{{2|JOURNAL}}}</em> {{{3|VOLUMEWITHNUMBER}}}: {{{5|PAGES}}}. {{#if:{…”的新页面</p>
<p><b>新页面</b></p><div><span id="{{{label}}}">{{{6|SURNAME1}}}({{{4|YEAR}}}{{{letter|}}}).</span> {{{1|TITLE}}} <em>{{{2|JOURNAL}}}</em> {{{3|VOLUMEWITHNUMBER}}}: {{{5|PAGES}}}. {{#if:{{{doi|}}}|[http://dx.doi.org/{{{doi|}}} doi:{{{doi|}}}]. |}}{{{preprint|}}}<noinclude><br />
{{Bibitem article N doc}}<br />
</noinclude></div>
Dongyu
https://wiki.swarma.org/index.php?title=%E6%A8%A1%E6%9D%BF:Bibitem_article_1&diff=34333&oldid=0
模板:Bibitem article 1
2024-03-05T04:05:15Z
<p>建立内容为“<span id="{{{label}}}">{{{6|SURNAME1}}}, {{{7|FORENAME1}}} ({{{4|YEAR}}}{{{letter|}}}).</span> {{{1|TITLE}}} <em>{{{2|JOURNAL}}}</em> {{{3|VOLUMEWITHNUMBER}}}: {{…”的新页面</p>
<p><b>新页面</b></p><div><span id="{{{label}}}">{{{6|SURNAME1}}}, {{{7|FORENAME1}}} ({{{4|YEAR}}}{{{letter|}}}).</span> {{{1|TITLE}}} <em>{{{2|JOURNAL}}}</em> {{{3|VOLUMEWITHNUMBER}}}: {{{5|PAGES}}}. {{#if:{{{doi|}}}|[http://dx.doi.org/{{{doi|}}} doi:{{{doi|}}}]. |}}{{{preprint|}}}<noinclude><br />
{{Bibitem article N doc}}<br />
</noinclude></div>
Dongyu
https://wiki.swarma.org/index.php?title=%E7%9F%AD%E6%9C%9F%E7%AA%81%E8%A7%A6%E5%8F%AF%E5%A1%91%E6%80%A7&diff=34332&oldid=34329
短期突触可塑性
2024-03-05T04:04:16Z
<p></p>
<a href="https://wiki.swarma.org/index.php?title=%E7%9F%AD%E6%9C%9F%E7%AA%81%E8%A7%A6%E5%8F%AF%E5%A1%91%E6%80%A7&diff=34332&oldid=34329">显示更改</a>
Dongyu
https://wiki.swarma.org/index.php?title=Sloppy_Model&diff=34328&oldid=34327
Sloppy Model
2024-03-05T03:40:02Z
<p><span dir="auto"><span class="autocomment">参考资料</span></span></p>
<table class="diff diff-contentalign-left diff-editfont-monospace" data-mw="interface">
<col class="diff-marker" />
<col class="diff-content" />
<col class="diff-marker" />
<col class="diff-content" />
<tr class="diff-title" lang="zh-Hans-CN">
<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">←上一版本</td>
<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">2024年3月5日 (二) 03:40的版本</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l67" >第67行:</td>
<td colspan="2" class="diff-lineno">第67行:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>==参考资料==</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>==参考资料==</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline"><ref></del></div></td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">{{Reflist}}</ins></div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">"Universally Sloppy Parameter Sensitivities in Systems Biology", Ryan N. Gutenkunst, Joshua J. Waterfall, Fergal P. Casey, Kevin S. Brown, Christopher R. Myers, James P. Sethna, PLoS Comput Biol 3(10) e189 (2007). (PLoS, doi:10.1371/journal.pcbi.0030189), pdf). [Reviewed in NewsBytes of Biomedical Computation Review (Winter 07/08).]</ref></del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline"><ref>"Sloppy model universality class and the Vandermonde matrix", Joshua J. Waterfall, Fergal P. Casey, Ryan N. Gutenkunst, Kevin S. Brown, Christopher R. Myers, Piet W. Brouwer, Veit Elser, and James P. Sethna, Phys. Rev. Letters 97, 150601 (2006), pdf.</ref></del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline"><ref>"Sloppy model universality class and the Vandermonde matrix", Joshua J. Waterfall, Fergal P. Casey, Ryan N. Gutenkunst, Kevin S. Brown, Christopher R. Myers, Piet W. Brouwer, Veit Elser, and James P. Sethna, Phys. Rev. Letters 97, 150601 (2006), pdf.</ref></del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline"><ref>Parameter Space Compression Underlies Emergent Theories and Predictive Models, Benjamin B. Machta, Ricky Chachra, Mark K. Transtrum, James P. Sethna, Science 342, 604-607 (2013). See also Physicists unify the structure of scientific theories in the Cornell Chronicle (Anne Ju).</ref></del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline"><ref>Jesse Silverberg's Huffington Post blog and Kathryn McGill's vlog Soft Matters with Jim Sethna from The Physics Factor.</ref></del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline"><ref>(Unedited) Interview of Sethna by Steven Reiner, Stony Brook School of Journalism, from a workshop by the Alan Alda Center for Communicating Science sponsored by the Kavli Institute at Cornell, May 2013. Mobile version.</ref></del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline"><ref>"Optimal experimental design in an EGFR signaling and down-regulation model", Fergal P. Casey, Dan Baird, Qiyu Feng, Ryan N. Gutenkunst, Joshua J. Waterfall, Christopher R. Myers, Kevin S. Brown, Richard A. Cerione, and James P. Sethna, IET Systems Biology 1, 190-202 (2007) (pdf).</ref></del></div></td><td colspan="2"> </td></tr>
<!-- diff cache key wiki:diff::1.12:old-34327:rev-34328 -->
</table>
Leshell0609