A simplified computational memory model from information processing

Abstract

This paper is intended to propose a computational model for memory from the view of information processing. The model, called simplified memory information retrieval network (SMIRN), is a bi-modular hierarchical functional memory network by abstracting memory function and simulating memory information processing. At first meta-memory is defined to express the neuron or brain cortices based on the biology and graph theories, and we develop an intra-modular network with the modeling algorithm by mapping the node and edge, and then the bi-modular network is delineated with intra-modular and inter-modular. At last a polynomial retrieval algorithm is introduced. In this paper we simulate the memory phenomena and functions of memorization and strengthening by information processing algorithms. The theoretical analysis and the simulation results show that the model is in accordance with the memory phenomena from information processing view.

Aging effects on the encoding/retrieval flip in associative memory: fMRI evidence from incidental contingency learning

“…Human knowledge and memory are traditionally conceptualized as cognitive information networks, consisting of nodes and edges of different degrees and weights, respectively ( Raaijmakers and Shiffrin, 1980 ; Eichenbaum, 2004 ; Zhang et al, 2016 ). The associative memory (AM) can be considered the basic “control module” of these “cognitive information networks,” which enables structural modifications (e.g., adding new information or modulating association strengths) as well as the retrieval of included information.…”