感知神经网络的信息传输及电路实现研究

Study of Information Transmission in Sensory Neural Networks and Circuit Experiment

为提高神经元网络信号的传输效率,改善人们对外界刺激的感知能力,本文采用平均互信息量的评价方法,研究了费茨休夫-南云感知神经元模型中信息传输的随机共振现象,探讨了并联单层神经元网络和双层神经元网络的信息处理功能,并用PSpice电路对神经元网络的信息传输进行了实验验证。数值模拟与电路仿真结果表明,FHN神经元并联模型和双层神经元网络具有随机共振现象,在一定的噪声强度范围内,双层神经元网络对输入信号的响应好于单层神经元网络,噪声能够提高神经元网络的输入输出互信息量,平均互信息量的最大值对应最优的传输性能,证明噪声对神经网络信息传输具有增强作用。该研究对感知神经元群体的信息传输具有重要的启示意义。

In order to improve the information transmission efficiency of neurons and facilitate man＇s adaptation to external stimuli,this paper studies the transmission of information-carrying signals in sensory FitzHugh-Nagumo （FHN） neuron model by the measure of average mutual information.The obtained results show that both single and bi-layer parallel FHN networks exhibit the stochastic resonance effect,which is also demonstrated in circuit experiments via PSpice software.It is proved that,as the internal noise intensity increases,the bilayer FHN neurons have a better response to the input signal,wherein the average mutual information can reach a maximum value in a certain range of noise intensity.Therefore,the transmission efficiency of the FHN network can be optimized at an optimal non-zero noise level.We argue that the present results are meaningful to the information-carrying signal transmission in sensory neurons.