脉动型神经元网络的联想记忆与分割

Associative memory and segmentation in the network composed of spiking neurons

以广泛讨论的Hodgk in-Hux ley神经元节点组成脉动神经元网络,从神经系统空时模式编码理论研究网络的记忆(或模式)存储与时间分割问题。给定一个输入模式,它是几种模式的叠加,网络能够以一部分神经元同步发放的形式一个接一个地在时间域分割出每一种模式。如果输入的模式是缺损的,系统能够把它们恢复成完好的原型,即神经网络的联想记忆功能。

We present in this paper some results on the temporal segmentation and retrieval of stored memories or patterns using neural networks composed of the widely used model neurons in the neuroscience society, the Hodgkin-Huxley neurons . For an input pattern which is an overlapped superposition of several stored patterns, it is shown that the proposed neuronal network model is capable of segmenting out each pattern one after another in the time domain as synchronous firings of a subgroup of neurons. 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.