Improved hippocampus-forehead cortex network space cognition method

Abstract

The invention discloses an improved hippocampus-forehead cortex network space cognition method, which comprises the following steps: firstly, a robot explores an environment to collect information, and hippocampus position cells receive the environment information and form a class topology cognition map; cortical column neurons in the forehead cortex receive and integrate hippocampus information projection and environmental award stimulation signals to evaluate the class topology cognitive map, in the evaluation process, if the robot detects that the environment is dynamically changed, the emergency change module is excited, and if multiple targets exist in the environment, the award re-estimation module is excited; finally, stimulating signals are diffused in the model in the form of neural activity waves, after evaluation is completed, reward stimulating signals are diffused in the forehead cortex network, synaptic connection between cortical column neurons in the forehead cortex is changed according to an STDP rule, and a global vector field is formed; and finally, target-oriented space navigation of the robot is realized.

Description

technical field [0001] The invention belongs to the field of bionics technology and artificial intelligence, and in particular relates to a mobile robot spatial cognition navigation model based on the hippocampus-prefrontal cortex when the environment changes. Background technique [0002] In recent years, drawing lessons from the basic principles of brain information processing, developing brain-inspired algorithms and developing brain-like intelligent systems are important areas of artificial intelligence and robotics research. It is one of the popular research directions to construct a cognitive model that imitates the mechanism of multiple brain areas of animals working together and copy it on the robot body, which is widely used in the fields of spatial cognition navigation and outdoor automatic driving in unknown environments. [0003] Spatial cognitive navigation relies on a tightly connected network of structures, including the hippocampus, prefrontal cortex, and oth...

AI Technical Summary

Problems solved by technology

However, these models perform poorly in the face of two types of tasks (revaluation task, emergency change task) for evaluating brain-inspired models, i.e., the adaptability and robustness of the models become weaker in complex environments.

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