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An Effective Implementation of Detection and Retrieval Property of Episodic Memory

Published: 12 January 2023 Publication History

Abstract

A deep understanding of the brain can lead to significant breakthroughs in Artificial Intelligence. Many researchers concentrate their efforts on simulating the human mind to comprehend its complexities better. With the intention of better understanding the episodic memory aspect of the human mind, we propose a deep learning model to implement the detection and retrieval properties of human episodic memory, a part of long-term memory. A model based on LSTM and CNN is proposed, which follows the architectural methodology of Rosenblatt’s experiential memory model. A comparison of detection efficiency and accuracy and the proposed model’s retrieval property with a recently suggested method demonstrate its effectiveness and superiority.

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    ICAAI '22: Proceedings of the 6th International Conference on Advances in Artificial Intelligence
    October 2022
    164 pages
    ISBN:9781450396943
    DOI:10.1145/3571560
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 12 January 2023

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    Author Tags

    1. Episodic Memory
    2. Long Short-Term Memory
    3. Recurrent Convolutional Networks
    4. Rosenblatt’s Experiential Memory Model

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