Analysis of Structure and Dynamics in Three-Neuron Motifs
Analysis of Structure and Dynamics in Three-Neuron Motifs
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DOI：
10.3389/fncom.2019.00005
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发表时间：
2019-02-07
影响因子：
3.2
通讯作者：
Metzner, Claus
中科院分区：
文献类型：
--
作者：
Krauss, Patrick;Zankl, Alexandra;Metzner, Claus
递归神经网络可以在没有任何驱动输入的情况下产生持续的状态到状态的转换，而这些转换的动力学特性由神经元连接强度决定。由于非线性，目前尚不清楚连接结构中离散的局部变化对系统动态的影响有多大，例如单个连接的移除、添加或符号切换。此外，没有合适的度量来量化两个具有任意索引的神经元的给定网络之间的结构和动力学差异。在这项工作中，我们提出了这样的排列不变度量，并将它们应用于三个具有离散三元连接强度的二元神经元的模体，这是生物网络中一类重要的构件。然后利用多维标度法研究了所有3,411个拓扑上不同的基序在结构和动力学方面的相似关系，揭示了强聚集和各种对称性。正如预期的那样，基序对之间的结构和动力学距离显示出显著的正相关。然而，引人注目的是，控制基序动力学的关键参数是兴奋性连接与抑制性连接的比率。
Recurrent neural networks can produce ongoing state-to-state transitions without any driving inputs, and the dynamical properties of these transitions are determined by the neuronal connection strengths. Due to non-linearity, it is not clear how strongly the system dynamics is affected by discrete local changes in the connection structure, such as the removal, addition, or sign-switching of individual connections. Moreover, there are no suitable metrics to quantify structural and dynamical differences between two given networks with arbitrarily indexed neurons. In this work, we present such permutation-invariant metrics and apply them to motifs of three binary neurons with discrete ternary connection strengths, an important class of building blocks in biological networks. Using multidimensional scaling, we then study the similarity relations between all 3,411 topologically distinct motifs with regard to structure and dynamics, revealing a strong clustering and various symmetries. As expected, the structural and dynamical distance between pairs of motifs show a significant positive correlation. Strikingly, however, the key parameter controlling motif dynamics turns out to be the ratio of excitatory to inhibitory connections.