Binocular vision depth computation system and brain-inspired computation model

An effector perception model, wherein an interaction between an external effector (for example a joint formed by muscles and bones) and an internal neural network is formed as neuronal action patterns or memories within the neural network, that is, an efficient connection is established between sensory neurons and motor planning neurons;

• the external effector form a loop connection with the sensory neurons and the action planning neurons;
• a signal formed under a stimulus are transmitted at high frequency in the loop;
• a neuronal connection between the sensory neurons and the motion planning neurons become efficient under an action of the high-frequency signals.

A logical perception model, wherein an interaction between external logic and an internal neural network forms an logical association within the neural network, an operation of the external logic forms a loop connection with the neural network, a signal driven by a stimulus is transmitted at high frequency in the loop;

• inter-neuron connections mapped from the logical cause-and-effect relationship within the neural network become efficient under the action of the high-frequency signals.

A binocular vision depth computation system and a brain-inspired computation model, wherein a pair of visual receptors perform the minor and irregular reciprocating motions to input stimuli into the neural network;

• a disparity signal is output through coordinative processing of the visual receptor, the horizontal inhibitory neuron model, the visual sequence model, the basic neuron model (perception model), the general sequence model, and the sequence matching model;
• the visual receptor, the horizontal inhibitory neuron model, the visual sequence model, the basic neuron model (perception model), the general sequence model, the sequence matching model, the effector perception model, and the logical perception model are basic brain-inspired computation models.