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In recent years, the interdisciplinary research between information scienceand neuroscience has been a hotspot. In this paper, based on recent biologicalfindings, we proposed a new model to mimic visual information processing, motorplanning and control in central and peripheral nervous systems of human. Mainsteps of the model are as follows: 1) Simulating "where" pathway in human: theSelective Search method is applied to simulate the function of human dorsalvisual pathway to localize object candidates; 2) Simulating "what" pathway inhuman: a Convolutional Deep Belief Network is applied to simulate thehierarchical structure and function of human ventral visual pathway for objectrecognition; 3) Simulating motor planning process in human: habitual motionplanning process in human is simulated, and motor commands are generated fromthe combination of control signals from past experiences; 4) Simulating precisemovement control in human: calibrated control signals, which mimic theadjustment for movement from cerebellum in human, are generated and updatedfrom calibration of movement errors in past experiences, and sent to themovement model to achieve high precision. The proposed framework mimicsstructures and functions of human recognition, visuomotor coordination andprecise motor control. Experiments on object localization, recognition andmovement control demonstrate that the new proposed model can not onlyaccomplish visuomotor coordination tasks, but also achieve high precisionmovement with learning ability. Meanwhile, the results also prove the validityof the introduced mechanisms. Furthermore, the proposed model could begeneralized and applied to other systems, such as mechanical and electricalsystems in robotics, to achieve fast response, high precision movement withlearning ability.

Biologically inspired model simulating visual pathways and cerebellum  function in human - Achieving visuomotor coordination and high precision  movement with learning ability