精神分裂症发展的自适应动力系统模型:表观遗传学与虚假记忆

IF 2.1 3区 心理学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Ilma Jaganjac , Sophie C.F. Hendrikse , Jan Treur
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引用次数: 0

摘要

本文介绍了以自建模网络模型为代表的五阶自适应性动力系统模型,以了解表观遗传因素在精神分裂症发展过程中的作用,尤其是关于错误记忆的发生。精神分裂症影响着全球相当一部分人口,其症状横跨认知、情感和社会领域,带来了复杂的挑战。本文的核心内容是探讨表观遗传对精神分裂症发展的影响,重点是这些表观遗传因素如何导致产生虚假记忆这一精神分裂症的显著症状。该模型假定早期的不良经历会严重影响海马体的发育,而海马体对记忆过程至关重要。通过采用以自建模网络为代表的五阶自适应性动力系统模型,本文研究了精神分裂症的遗传倾向、环境因素和表观遗传修饰之间错综复杂的动态关系。该模型的复杂性使得人们能够细致入微地理解基因与环境之间的相互作用,尤其突出了早期生活创伤在塑造终生认知结果中的作用。这种方法为分析精神分裂症的多面性提供了一个全面的框架,有助于加深对其病因和进展的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An adaptive dynamical system model for development of schizophrenia: Epigenetics and false memories
This paper introduces a fifth-order adaptive dynamical system model represented as a self-modelling network model to understand the role of epigenetic factors in developing schizophrenia, particularly about the occurrence of false memories. Schizophrenia, affecting a significant portion of the global population, presents complex challenges due to its symptoms across cognitive, emotional, and social domains. Central to this paper is the exploration of epigenetic influences on the development of schizophrenia, with a focus on how these epigenetic factors contribute to the generation of false memories, a notable symptom of the disorder. The model assumes that early adverse experiences significantly affect hippocampal development, which is crucial for memory processes. By employing a fifth-order adaptive dynamical system model represented as a self-modelling network, this paper examines the intricate dynamics between genetic predispositions, environmental factors, and epigenetic modifications in schizophrenia. The model’s sophistication allows for a nuanced understanding of the gene-environment interactions, particularly highlighting the role of early-life trauma in shaping lifelong cognitive outcomes. This approach offers a comprehensive framework for analyzing the multifaceted nature of schizophrenia, contributing to a deeper understanding of its etiology and progression.
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来源期刊
Cognitive Systems Research
Cognitive Systems Research 工程技术-计算机:人工智能
CiteScore
9.40
自引率
5.10%
发文量
40
审稿时长
>12 weeks
期刊介绍: Cognitive Systems Research is dedicated to the study of human-level cognition. As such, it welcomes papers which advance the understanding, design and applications of cognitive and intelligent systems, both natural and artificial. The journal brings together a broad community studying cognition in its many facets in vivo and in silico, across the developmental spectrum, focusing on individual capacities or on entire architectures. It aims to foster debate and integrate ideas, concepts, constructs, theories, models and techniques from across different disciplines and different perspectives on human-level cognition. The scope of interest includes the study of cognitive capacities and architectures - both brain-inspired and non-brain-inspired - and the application of cognitive systems to real-world problems as far as it offers insights relevant for the understanding of cognition. Cognitive Systems Research therefore welcomes mature and cutting-edge research approaching cognition from a systems-oriented perspective, both theoretical and empirically-informed, in the form of original manuscripts, short communications, opinion articles, systematic reviews, and topical survey articles from the fields of Cognitive Science (including Philosophy of Cognitive Science), Artificial Intelligence/Computer Science, Cognitive Robotics, Developmental Science, Psychology, and Neuroscience and Neuromorphic Engineering. Empirical studies will be considered if they are supplemented by theoretical analyses and contributions to theory development and/or computational modelling studies.
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