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A simplified computational memory model from information processing

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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.


Simulation of memory network model by modeling algorithm.The output of the model is the root of the intra-modular structure in the network regardless of the inter-modular structure.
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f4: Simulation of memory network model by modeling algorithm.The output of the model is the root of the intra-modular structure in the network regardless of the inter-modular structure.

Mentions: We make SMIRN memory the new words with a paragraph by the modeling algorithm in order to show the image of our model. After that, we verify the retrieval algorithm efficiency by retrieval algorithm. In word memorization, a new word as a path will be added to the model, and the repeating words or the interrelated words can strengthen the network and the path will be shorter. In the simulation, we select the ABSTRACT paragraph as memory tasks of the paper refs 2,7,8. We suppose that only one cortical takes part in the model and all the neurons connect to the cortical. The neuron and the cortical all can be looked on as meta-memory, and they are different in the meaning but no effect on the path computation from information processing view. In the figure, the center node is the root node (sn) where the memory information is imputed in or outputted from the network. Every node stands for a letter and every branch stands for a word, the different branches beginning from the root to the treetop stand for different words. The SMIRN of all the words in ABSTRACT paragraph of Ref. 7 is Fig. 4 by modeling algorithm2.


A simplified computational memory model from information processing
Simulation of memory network model by modeling algorithm.The output of the model is the root of the intra-modular structure in the network regardless of the inter-modular structure.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f4: Simulation of memory network model by modeling algorithm.The output of the model is the root of the intra-modular structure in the network regardless of the inter-modular structure.
Mentions: We make SMIRN memory the new words with a paragraph by the modeling algorithm in order to show the image of our model. After that, we verify the retrieval algorithm efficiency by retrieval algorithm. In word memorization, a new word as a path will be added to the model, and the repeating words or the interrelated words can strengthen the network and the path will be shorter. In the simulation, we select the ABSTRACT paragraph as memory tasks of the paper refs 2,7,8. We suppose that only one cortical takes part in the model and all the neurons connect to the cortical. The neuron and the cortical all can be looked on as meta-memory, and they are different in the meaning but no effect on the path computation from information processing view. In the figure, the center node is the root node (sn) where the memory information is imputed in or outputted from the network. Every node stands for a letter and every branch stands for a word, the different branches beginning from the root to the treetop stand for different words. The SMIRN of all the words in ABSTRACT paragraph of Ref. 7 is Fig. 4 by modeling algorithm2.

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.