  The neural mechanism of memory has a very close relation with the problem of
representation in artificial intelligence. In this paper a computational model
was proposed to simulate the network of neurons in brain and how they process
information. The model refers to morphological and electrophysiological
characteristics of neural information processing, and is based on the
assumption that neurons encode their firing sequence. The network structure,
functions for neural encoding at different stages, the representation of
stimuli in memory, and an algorithm to form a memory were presented. It also
analyzed the stability and recall rate for learning and the capacity of memory.
Because neural dynamic processes, one succeeding another, achieve a
neuron-level and coherent form by which information is represented and
processed, it may facilitate examination of various branches of Artificial
Intelligence, such as inference, problem solving, pattern recognition, natural
language processing and learning. The processes of cognitive manipulation
occurring in intelligent behavior have a consistent representation while all
being modeled from the perspective of computational neuroscience. Thus, the
dynamics of neurons make it possible to explain the inner mechanisms of
different intelligent behaviors by a unified model of cognitive architecture at
a micro-level.
