The mapping of the wiring diagrams of neural circuits promises to allow us to link the structure and function of neural networks. Current approaches to analyzing such connectomes rely mainly on graph-theoretical tools, but these may downplay the complex nonlinear dynamics of single neurons and the way networks respond to their inputs. Here, we measure the functional similarity of simulated networks of neurons, by quantifying the similitude of their spiking patterns in response to the same stimuli. We find that common graph-theory metrics convey little information about the similarity of networks’ responses. Instead, we learn a functional metric between networks based on their synaptic differences and show that it accurately predicts the similarity of novel networks, for a wide range of stimuli. We then show that a sparse set of architectural features—the sum of synaptic inputs that each neuron receives and the sum of each neuron’s synaptic outputs—predicts the functional similarity of networks of up to 1000 neurons, with high accuracy. We thus suggest new architectural design principles that shape the function of neural networks. These architectural features conform with experimental evidence of homeostatic synaptic mechanisms.

中文翻译：

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学习预测神经网络功能相似性的架构特征

神经电路接线图的映射有望让我们将神经网络的结构和功能联系起来。当前分析此类连接组的方法主要依赖于图论工具，但这些工具可能会淡化单个神经元的复杂非线性动力学以及网络对其输入的响应方式。在这里，我们通过量化它们响应相同刺激的尖峰模式的相似性来测量模拟神经元网络的功能相似性。我们发现常见的图论指标传达的关于网络响应相似性的信息很少。相反，我们根据它们的突触差异学习网络之间的功能度量，并表明它准确地预测了新网络的相似性，适用于广泛的刺激。然后，我们展示了一组稀疏的架构特征——每个神经元接收的突触输入的总和和每个神经元的突触输出的总和——预测多达 1000 个神经元的网络的功能相似性，具有高精度。因此，我们提出了塑造神经网络功能的新架构设计原则。这些架构特征符合稳态突触机制的实验证据。