Neuron oscillator and chaotic neutral network based on the same

Abstract

The invention discloses a neuron oscillator, namely a Lee-oscillator. The Lee-oscillator comprises excitation neurons and inhibitory neurons. The excitation neurons are used for receiving inhibitory signals from the inhibitory neurons. The inhibitory neurons are used for receiving excitation signals from the excitation neurons. Excitation self-feedback respectively exists in the excitation neurons and the inhibitory neurons. The Lee-oscillator further comprises input neurons and output neurons. The output neurons are used for receiving excitation signals from the input neurons, the excitation signals from the excitation neurons and the inhibitory signals from the inhibitory neurons. The input neurons and the excitation neurons are respectively used for receiving stimulation of external input. The Lee-oscillator has continuity of neural dynamics and provides actual gradual changing from chaotic dynamics to non-chaotic dynamics. Additionally provided is an instant chaotic self-associative network based on the Lee-oscillator.

Description

technical field [0001] The present invention relates to the field of neuroscience, in particular, to a neuron oscillator and a chaotic neural network based on the neuron oscillator, more specifically, to a Lee-oscillator (Lee-oscillator) and a Lee-oscillator Transient chaotic self-associative networks of . Background technique [0002] Biological neuron oscillators refer to living tissues that can produce oscillatory behavior, and they are often composed of a large number of neuron cells coupled with each other. In order to establish a mathematical model of a biological neuron oscillator, an artificial neuron oscillator composed of a small number of artificial neurons (hereinafter referred to as a neuron oscillator) is proposed to simulate similar functions, and then a neural network is formed by neuron oscillators. [0003] In the past few decades, for a variety of different application environments, ranging from simple synaptic memory encoding problems to complex pattern ...

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Problems solved by technology

However, if figure 2 As shown in the bifurcation diagram in , in a single Wang oscillator, there is a stable quiescent state (i.e., Z=0) in the bifurcation dynamics, which also makes the oscillator ineffective as a BTU or for time information encoding

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