Method for realizing causal inference through nerve circuit, and nerve circuit

Abstract

The invention discloses a method for realizing causal inference through a nerve circuit, and the nerve circuit. The method comprises steps that probability distribution of multiple stimuli is coded, N input nerve elements are emitted, an average emission rate of each input nerve element is acquired; the total emission rate of the N input nerve elements is calculated, the average emission rate of each input nerve element after normalization can be acquired according to the total emission rate; the N nerve elements are emitted to a first emission nerve element and a second emission nerve element according to sources of the multiple stimuli, and synaptic weights of the first and second emission nerve elements are acquired; output emission rates of the first emission nerve element and the second emission nerve element are acquired according the average emission rate of each nerve element after normalization; the output emission rates of the first emission nerve element and the second emission nerve element are compared, and the emission nerve element having the largest output emission rate is acquired. Through the method, causal inference for similarity determination on multiple factors is facilitated through the nerve circuit.

Description

technical field [0001] The invention relates to the technical field of neural circuits, in particular to a method for realizing causal reasoning through neural circuits and the neural circuit. Background technique [0002] The human brain can judge the source of the stimulus, such as visual stimulation and auditory stimulation, and this judgment process is the process of the human brain to realize causal reasoning. [0003] At present, with the development of artificial intelligence technology, some functions of the human brain can be realized through artificial models. However, research on the neural circuits that enable causal inference is still immature. The causal inference model built in related technologies has a very complicated calculation process and structure, which is difficult to realize through neural circuits, and the current model can only realize causal inference from two stimulus sources, so the practical application value is very low. It can be seen that ...

AI Technical Summary

Problems solved by technology

However, research on the neural circuits that enable causal inference is still immature

The causal inference model built in related technologies has a very complicated calculation process and structure, which is difficult to realize through neural circuits, and the current model can only realize the causal inference of two stimulus sources, so the practical application value is very low

It can be seen that it is currently difficult to achieve causal inference through neural circuits

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