Weak signal sensing method based on neuron small-world network stochastic resonance

Abstract

The invention discloses a weak signal sensing method based on neuron small-world network stochastic resonance, and the method comprises the following steps: firstly improving the parameter setting of an FHN neuron calculation model, and changing the traditional condition that membrane potential and recovery variable feature time are set to be the same numerical value for the consideration of simple calculation; enabling the improved parameter setting method to enhance the potential function barrier of the membrane potential, so that the transition probability between potential wells is improved; then constructing an FHN neuron calculation model containing weak signal input and background noise, setting the FHN neuron calculation model as small-world network nodes, and giving and realizing a dynamic synaptic interconnection rule representing the relationship among the nodes; for the output of each node of the small-world network, using a mean value fusion method based on cross correlation coefficients, and subjecting the output of each node of the network to screening and information fusion, so that the performance and robustness of the system are improved, and the weak signal sensing effect in the sense of the small-world network system is obtained.

Description

technical field [0001] The invention belongs to the application field of neural computing models, and in particular relates to a weak signal perception method based on stochastic resonance of a neuron small-world network. Background technique [0002] Visual neurophysiological experiments and computational simulations have shown that there is a stochastic resonance mechanism in the nervous system, that is, using the nonlinear characteristics of neural encoding and dynamic synaptic connections to convert part of the background noise energy of the nervous system into weak signal energy, thereby realizing weak signal energy. perception. At present, many studies have applied the stochastic resonance mechanism to the processing of weak signals, but most of the nonlinear systems selected are ideal physical systems represented by bistable states, or single neuron computing models. The nervous system is simplified, but the representation, nonlinear fitting and anti-interference abi...

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Problems solved by technology

At present, many studies have applied the stochastic resonance mechanism to the processing of weak signals, but most of the nonlinear systems selected are ideal physical systems represented by bistable states, or single neuron computing models. Simplify the nervous system, but ignore the representation, nonlinear fitting and anti-interference ability of neuron clusters, so the internal mechanism of stochastic resonance in the process of visual and auditory perception in the nervous system has not been fully explored and applied

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