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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 required for building intelligent computation. Experiments support many forms and sizes of neural clustering, while neural mass models (NMM) assume scale-invariant functionality. Here, we use computational simulations with brain-derived fMRI network to show that not only brain network stays structurally self-similar continuously across scales, but also neuron-like signal integration functionality is preserved at particular scales. As such, we propose a coarse-graining of network of neurons to ensemble-nodes, with multiple spikes making up its ensemble-spike, and time re-scaling factor defining its ensemble-time step. The fractal-like spatiotemporal structure and function that emerge permit strategic choice in bridging across experimental scales for computational modeling, while also suggesting regulatory constraints on developmental and/or evolutionary "growth spurts" in brain size, as per punctuated equilibrium theories in evolutionary biology.