Fundamental law underlying predictive remapping

Predictive remapping (PRE )—the ability of cells in retinotopic brain structures to transiently exhibit spatiotemporal shifts beyond the spatial extent of their classical anatomical receptive fields—has been proposed as a primary mechanism that stabilizes an organism’s percept of the visual world around the time of a saccadic eye movement. Despite the well-documented effects of PRE , a biologically plausible mathematical framework that specifies a fundamental law and the functional neural architecture that actively mediates this ubiquitous phenomenon does not exist. We introduce the Newtonian model of PRE , where each modular component of PRE manifests as three temporally overlapping forces: centripetal ( fC ), convergent ( fP ), and translational ( fT ), that perturb retinotopic cells from their equilibrium extent. The resultant and transient influences of these forces fC + fP + fT gives rise to a neuronal force field that governs the spatiotemporal dynamics of PRE . This neuronal force field fundamentally obeys an inverse-distance law PRE ∝ 1 r1.6 , akin to Newton’s law of universal gravitation [I. Newton, Newton’s Principia: The Mathematical Principles of Natural Philosophy (Geo. P. Putnam, New-York, 1850)] and activates retinotopic elastic fields elϕ’s. We posit that elϕ’s are transient functional neural structures that are self-generated by visual systems during active vision and approximate the sloppiness (or degrees of spatial freedom) within which receptive fields are allowed to shift, while ensuring that retinotopic organization does not collapse. The predictions of this general model are borne out by the spatiotemporal changes in visual sensitivity to probe stimuli in human subjects around the time of an eye movement and qualitatively match neural sensitivity signatures associated with predictive shifts in the receptive fields of cells in premotor and higher-order retinotopic brain structures. The introduction of this general model opens the search for possible biophysical implementations and provides experimentalists with a simple, elegant, yet powerful mathematical framework they can now use to generate experimentally testable predictions across a range of biological systems.