| The fundamental challenge in visual motion processing is how the brain extracts velocity vectors unambiguously. While the traditional energy models rely on symmetrical nonlinear operations to preserve signal energy, they face inherent mathematical constraints in maintaining the independence of velocity information. We present an innovative mathematical framework utilizing Wiener kernel expansion and spatio-temporal Jacobians to illustrate how neural networks address the different velocities separately and achieve reliable velocity perception. It is shown that our proposed asymmetric network model—characterized by odd/even-order nonlinearities and phase shift in the nonlinear pathway in which spatiotemporal physical velocity is effectively transformed into the phase shifts on the time domain. Our proposals suggest that the biological nuisance of neural variability and structural asymmetry is, in fact, a fundamental computational resource required to resolve mathematical singularities and achieve unambiguous three-dimensional motion perception. |
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