Neural Network Models of Vector Coding, Learning, and Trajectory Formation During Planned and Reactive Arm and Eye Movements

摘要

Contemporary neural network models provide insights into some of the organizational principles that govern biological sensory-motor systems, and offer a level of computational precision that enables sharp comparisons and contrasts to be made between different sensory-motor systems. The capacity of these models to clarify, integrate, and predict behavioral and neural data is predicated upon the coordinated use of theoretical, mathematical, computational and empirical tools in a manner that reveals many more constraints on brain design than empirical tools alone. No single experimental paradigm in the behavioral and brain sciences provides sufficiently many data to uniquely characterize a neural system. Interdisciplinary theoretical and empirical approaches that can coordinate and discover both top-down and bottom-up constraints at multiple levels of behavioral and neural organization provide a much greater level of guidance towards characterizing brain designs. The present chapter takes as its point of departure one important design principle that has been clarified by such an interdisciplinary approach. This is the principle of vector encoding that has been described, for example, in both the control of saccadic eye movements by the superior colliculus and the control of arm movements by the motor cortex. Keywords: Neural networks; Eye movement; Arm movement; Robotics; Self-organization; Learning; Trajectory formation; Planning; Vector coding. (JHD)