This short position paper claims that computational neuroscience should refocus on the study of multiscale spatiotemporal shapes (STS) of activity in large neural populations. Instead of naive engineering metaphors, which view the brain as a signal-processing channel traversed by "information", or neo-Behaviorist probabilistic frameworks, where it is a "gray box" tuned by environmental distributions, new theories should resolutely promote mechanistic, complex systems models. In this paradigm, massively recurrent networks should support the spontaneous (and triggered) emergence of intrinsic dynamical regimes, made of myriads of correlated electrophysiological signals—not unlike other collective biological phenomena such as bird flocks, insect constructions, or morphogenesis. "Neuron flocking", for its part, must happen in phase space and across a complex network topology: Can we characterize the "shapes" and composition laws of these mind states, upon which high-level symbolic computing can ultimately rest?