神经系统中随机和混沌感知信号的联想记忆与分割

Associative memory and segmentation in the neural system under stimulation of stochastic or chaotic perceptual singals

为了模拟人与动物感知信息的真实环境,以脉动神经元节点组成神经元网络,研究在随机刺激和混沌刺激等极端条件下的记忆模式存储与时间分割问题.研究表明:网络对于若干种模式的叠加输入,能够以一部分神经元同步发放的形式在时间域上分割出每一模式.如果输入模式是缺损的,系统能够把它们恢复到原型,即具有联想记忆功能.通过调节耦合强度和噪声强度等参数使得网络在中等强度噪声达到最优的时间分割,与广泛讨论的随机共振现象一致.

We present in this paper some results on the temporal segmentation and retrieval of stored memories or patterns using neural networks composed of spiking neurons. Respecting the working environment, we present the network with stochastic or chaotic stimuli as their extremely working conditions and also with noise. We attempt to give an explanation to the function of memory retrieval of the brain system, where the stimuli usually may not be constant, sinusoidal or periodic, but rather chaotic or stochastic. For an input pattern which is a superposition of several stored patterns, it is shown that the proposed neuronal network model is capable of segmenting out each pattern one after another as synchronous firings of a subgroup of neurons, and if a corrupted input pattern is presented, the network is shown to be able to retrieve the perfect one, that is it has the function of associative memory. By thorougly adjusting the parameters, such as the coupling strength and the intensity of the noise, the temporal segmentation attains its optimal performance at intermediate noise intensity, which reminds of the stochastic resonance observed in the coupled spiking neuronal networks.