Esposito Umberto, Giugliano Michele, van Rossum Mark, Vasilaki Eleni
Department of Computer Science, University of Sheffield, Sheffield, United Kingdom.
Department of Computer Science, University of Sheffield, Sheffield, United Kingdom; Theoretical Neurobiology and Neuroengineering Laboratory, Department of Biomedical Sciences, University of Antwerp, Wilrijk, Belgium; Laboratory of Neural Microcircuitry, Brain Mind Institute, École polytechnique fédérale de Lausanne, Lausanne, Switzerland.
PLoS One. 2014 Jul 9;9(7):e100805. doi: 10.1371/journal.pone.0100805. eCollection 2014.
Cognitive functions are stored in the connectome, the wiring diagram of the brain, which exhibits non-random features, so-called motifs. In this work, we focus on bidirectional, symmetric motifs, i.e. two neurons that project to each other via connections of equal strength, and unidirectional, non-symmetric motifs, i.e. within a pair of neurons only one neuron projects to the other. We hypothesise that such motifs have been shaped via activity dependent synaptic plasticity processes. As a consequence, learning moves the distribution of the synaptic connections away from randomness. Our aim is to provide a global, macroscopic, single parameter characterisation of the statistical occurrence of bidirectional and unidirectional motifs. To this end we define a symmetry measure that does not require any a priori thresholding of the weights or knowledge of their maximal value. We calculate its mean and variance for random uniform or Gaussian distributions, which allows us to introduce a confidence measure of how significantly symmetric or asymmetric a specific configuration is, i.e. how likely it is that the configuration is the result of chance. We demonstrate the discriminatory power of our symmetry measure by inspecting the eigenvalues of different types of connectivity matrices. We show that a Gaussian weight distribution biases the connectivity motifs to more symmetric configurations than a uniform distribution and that introducing a random synaptic pruning, mimicking developmental regulation in synaptogenesis, biases the connectivity motifs to more asymmetric configurations, regardless of the distribution. We expect that our work will benefit the computational modelling community, by providing a systematic way to characterise symmetry and asymmetry in network structures. Further, our symmetry measure will be of use to electrophysiologists that investigate symmetry of network connectivity.
认知功能存储在连接组中，即大脑的布线图，它具有非随机特征，即所谓的基序。在这项工作中，我们关注双向对称基序，即两个神经元通过强度相等的连接相互投射，以及单向非对称基序，即在一对神经元中只有一个神经元投射到另一个神经元。我们假设这些基序是通过依赖活动的突触可塑性过程形成的。因此，学习使突触连接的分布远离随机性。我们的目标是提供一个全局的、宏观的、单参数的双向和单向基序统计出现情况的表征。为此，我们定义了一种对称度量，它不需要对权重进行任何先验阈值设定或了解其最大值。我们计算了随机均匀分布或高斯分布的均值和方差，这使我们能够引入一种置信度度量，以衡量特定配置的对称或不对称程度，即该配置是偶然结果的可能性。我们通过检查不同类型连接矩阵的特征值来证明我们对称度量的辨别能力。我们表明，高斯权重分布使连接基序比均匀分布更偏向于对称配置，并且引入随机突触修剪，模拟突触发生中的发育调节，使连接基序更偏向于不对称配置，而与分布无关。我们期望我们的工作将通过提供一种系统的方法来表征网络结构中的对称和不对称性，从而使计算建模社区受益。此外，我们的对称度量将对研究网络连接对称性的电生理学家有用。
