Physics and Astronomy Department, Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.
Laboratory for Computational Neurodiagnostics, Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA.
Sci Rep. 2021 Mar 5;11(1):5331. doi: 10.1038/s41598-021-82461-4.
Brains demonstrate varying spatial scales of nested hierarchical clustering. Identifying the brain's neuronal cluster size to be presented as nodes in a network computation is critical to both neuroscience and artificial intelligence, as these define the cognitive blocks capable of building intelligent computation. Experiments support various forms and sizes of neural clustering, from handfuls of dendrites to thousands of neurons, and hint at their behavior. Here, we use computational simulations with a brain-derived fMRI network to show that not only do brain networks remain structurally self-similar across scales but also neuron-like signal integration functionality ("integrate and fire") is preserved at particular clustering scales. As such, we propose a coarse-graining of neuronal networks to ensemble-nodes, with multiple spikes making up its ensemble-spike and time re-scaling factor defining its ensemble-time step. This fractal-like spatiotemporal property, observed in both structure and function, permits strategic choice in bridging across experimental scales for computational modeling while also suggesting regulatory constraints on developmental and evolutionary "growth spurts" in brain size, as per punctuated equilibrium theories in evolutionary biology.
大脑表现出嵌套层次聚类的不同空间尺度。在网络计算中，将大脑的神经元聚类大小识别为节点，这对神经科学和人工智能都至关重要，因为这些节点定义了能够构建智能计算的认知模块。实验支持从少量树突到数千个神经元的各种形式和大小的神经聚类，并暗示了它们的行为。在这里，我们使用基于大脑的 fMRI 网络的计算模拟表明，不仅大脑网络在不同尺度上保持结构自相似性，而且类似神经元的信号整合功能（“整合和发射”）在特定聚类尺度上得以保留。因此，我们提出将神经元网络粗粒化为集合节点，其中多个尖峰构成集合尖峰，时间重缩放因子定义其集合时间步长。这种分形状的时空特性，在结构和功能中都有观察到，允许在计算建模中跨越实验尺度进行策略选择，同时也暗示了对大脑大小的发育和进化“生长突增”的调节限制，正如进化生物学中的间断平衡理论所建议的那样。
