A Neural Dynamic Model based on Activation Diffusion and a Micro-Explanation for Cognitive Operations
This work aims to provide a unified, micro-level explanation for various intelligent behaviors by modeling cognitive manipulation from a computational neuroscience perspective, potentially facilitating examination in diverse AI branches.
This paper proposes a computational model that simulates neural networks in the brain, focusing on how neurons encode firing sequences and process information. It presents the network structure, encoding functions, stimulus representation, and a memory formation algorithm, analyzing stability, recall rate, and memory capacity.
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.