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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.


The intra-modular tree model in SMIRN with words memory of “netbeans” and “network”.The information is inputted into the superior node (sn) with the neuron node and the edge connection; paths form the intra-modular model; the output expresses the retrieval operations.
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f1: The intra-modular tree model in SMIRN with words memory of “netbeans” and “network”.The information is inputted into the superior node (sn) with the neuron node and the edge connection; paths form the intra-modular model; the output expresses the retrieval operations.

Mentions: Clearly, the intra-modular model is finally constructed as an inverted tree1725 (Fig. 1). The tree is the memorization form. The strengthening process will be realized by retrieval algorithm subsequently. Forgetting becomes when we can’t recall the content ever in our brain which is different from misremembering, and this process doesn’t need the neuron or cortex to participate in information processing because it is a passive process. Forgetting includes the partial forgetting and whole forgetting. We define a class of virtual node which has no effect on the construction process of SMIRN to describe forgetting function. The virtual node is tip node and end node from computer definition, which can enter the network with time, of course, it doesn’t exist in the anatomical structure of the brain represents the forgetting function in the memory information process. A virtual node joins the network similar to the addition of a new node to the network; the difference is that the virtual node connects the network from the lowest position, i.e., treetop, climbing to the upper step by step. At first, it replaces the last node and contacts the network; the partial forgetting begins to happen. Then, it replaces its parent node until to root; the whole forgetting comes. Of course, the virtual node hasn’t child node when it connects to the network or climbs to the upper, and the replaced node will be deleted. A virtual node along with its edge disappearing from the network is the the process of deleting, which is simpler than the strengthening process.


A simplified computational memory model from information processing
The intra-modular tree model in SMIRN with words memory of “netbeans” and “network”.The information is inputted into the superior node (sn) with the neuron node and the edge connection; paths form the intra-modular model; the output expresses the retrieval operations.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f1: The intra-modular tree model in SMIRN with words memory of “netbeans” and “network”.The information is inputted into the superior node (sn) with the neuron node and the edge connection; paths form the intra-modular model; the output expresses the retrieval operations.
Mentions: Clearly, the intra-modular model is finally constructed as an inverted tree1725 (Fig. 1). The tree is the memorization form. The strengthening process will be realized by retrieval algorithm subsequently. Forgetting becomes when we can’t recall the content ever in our brain which is different from misremembering, and this process doesn’t need the neuron or cortex to participate in information processing because it is a passive process. Forgetting includes the partial forgetting and whole forgetting. We define a class of virtual node which has no effect on the construction process of SMIRN to describe forgetting function. The virtual node is tip node and end node from computer definition, which can enter the network with time, of course, it doesn’t exist in the anatomical structure of the brain represents the forgetting function in the memory information process. A virtual node joins the network similar to the addition of a new node to the network; the difference is that the virtual node connects the network from the lowest position, i.e., treetop, climbing to the upper step by step. At first, it replaces the last node and contacts the network; the partial forgetting begins to happen. Then, it replaces its parent node until to root; the whole forgetting comes. Of course, the virtual node hasn’t child node when it connects to the network or climbs to the upper, and the replaced node will be deleted. A virtual node along with its edge disappearing from the network is the the process of deleting, which is simpler than the strengthening process.

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.