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A simplified computational memory model from information processing

View Article: PubMed Central - PubMed

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.

No MeSH data available.


The extreme connections in the inter-modular structure of SMIRN; the brain cortical regions as the super-node forms a connected ring (left) or a full connected network (right).
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f2: The extreme connections in the inter-modular structure of SMIRN; the brain cortical regions as the super-node forms a connected ring (left) or a full connected network (right).

Mentions: For inter-modular, it is well-known that many cortex take part in the memory process67, therefore, we design the inter-modular structure to describe the anatomical and functional connections of brain based on the intra-modular structure. Theoretically, a connected ring and a full connected network are the extremes to connect a network for the inter-modular structure (Fig. 2). According to previous studies6101819, we know that the network of inter-modular is not great because not all the brain cortical take part in the memory function. We conclude that the largest isn’t more than 90from anatomical automatic labeling (AAL) model with the brain dividing into 90 cortex29.


A simplified computational memory model from information processing
The extreme connections in the inter-modular structure of SMIRN; the brain cortical regions as the super-node forms a connected ring (left) or a full connected network (right).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5120294&req=5

f2: The extreme connections in the inter-modular structure of SMIRN; the brain cortical regions as the super-node forms a connected ring (left) or a full connected network (right).
Mentions: For inter-modular, it is well-known that many cortex take part in the memory process67, therefore, we design the inter-modular structure to describe the anatomical and functional connections of brain based on the intra-modular structure. Theoretically, a connected ring and a full connected network are the extremes to connect a network for the inter-modular structure (Fig. 2). According to previous studies6101819, we know that the network of inter-modular is not great because not all the brain cortical take part in the memory function. We conclude that the largest isn’t more than 90from anatomical automatic labeling (AAL) model with the brain dividing into 90 cortex29.

View Article: PubMed Central - PubMed

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.

No MeSH data available.