有效序列处理任务中时空记忆的吸引子性质

IF 1 Q4 OPTICS

Optical Memory and Neural Networks Pub Date : 2023-11-28 DOI:10.3103/S1060992X23060097

P. Kuderov, E. Dzhivelikian, A. I. Panov

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引用次数: 0

摘要

对于自主人工智能系统来说，对时空信息进行有效的编码和记忆、提取和重用是非常重要的。对于自然智能来说，什么是自然的，对人工智能系统来说仍然是一个挑战。本文提出了一个具有吸引子模块的时空记忆生物学模型，并研究了其编码序列和有效提取和重用重复模式的能力。在合成数据、文本数据和分布式摄像机数据上的实验结果表明，使用吸引子模块后，模型的性能得到了质的改善。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Attractor Properties of Spatiotemporal Memory in Effective Sequence Processing Task

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Attractor Properties of Spatiotemporal Memory in Effective Sequence Processing Task

For autonomous AI systems, it is important to process spatiotemporal information to encode and memorize it and extract and reuse abstractions effectively. What is natural for natural intelligence is still a challenge for AI systems. In this paper, we propose a biologically plausible model of spatiotemporal memory with an attractor module and study its ability to encode sequences and efficiently extract and reuse repetitive patterns. The results of experiments on synthetic and textual data and data from DVS cameras demonstrate a qualitative improvement in the properties of the model when using the attractor module.

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来源期刊

Optical Memory and Neural Networks OPTICS-

CiteScore

1.50

自引率

11.10%

发文量

期刊介绍： The journal covers a wide range of issues in information optics such as optical memory, mechanisms for optical data recording and processing, photosensitive materials, optical, optoelectronic and holographic nanostructures, and many other related topics. Papers on memory systems using holographic and biological structures and concepts of brain operation are also included. The journal pays particular attention to research in the field of neural net systems that may lead to a new generation of computional technologies by endowing them with intelligence.