Morphic Computing Inspired Hopfield Model for Sensorimotor Transformation (Vestibule-Ocular System)

Abstract

It is strongly evidenced that dendritic arbors of neurons reveal fractal geometries which is non-euclidean [1]. For example, The vestibular-Ocular system receive information by the vestibular system as input and after transform the three dimensional sensors into the six motors or muscles that control the eyes movement as output. Now the output system con be representer in euclidean three dimension space but this create a lost of the problems. With the inspiration from morphic computing [13-24], we argue that the brain changes the local geometry of the output system in a way to take care of the nonlinearity of the muscles system. Brain compensates the nonlinear geometry by suitable adaptive point of view or geometry. In this paper we give a more general representation of the morphic computing inspired Hopfield-like neural net model. The quantum Hopfield neural net model and quantum holography can be extended to nonorthogonal pattern code which geometry change in anytime. In the traditional Hopfield system geometry is always the same Euclidean with fixed metric.