Bioinspired computing nets for directionality in vision

Abstract

Directional selectivity to local visual stimuli appears in various levels of the visual pathway, being in the retina very conspicuous. Neurophysiology suggests that directionality (as well as other local and quasi-global filtering properties) are based in the space–time interactions of processes with different “memory” (latency). We draw inspiration from the corresponding underlying biological mechanisms to propose two general schemes for directionality computation in nets, compatible with other space–time filtering properties. First, a connectivistic mechanism based on bipolar–amacrine–ganglion cell interaction is proposed, by formalizing the classical proposals of early vision neurophysiologists. Second, inspired initially in the more recently described intrinsic directionality of amacrines, novel schemes are proposed where directionality appear as the computing consequence of adding memory to spatial filtering structures. The mathematical formulations are achieved by means of Newton Filters and Hermite Functionals.