重塑计算神经精神病学超越突触症
Hugo Bottemanne1,2,3, Stephane Mouchabac3,4, Christophe Gauld5,6
1MOODS Team, INSERM 1018, CESP (Centre de Recherche en Epidémiologie et Santé des Populations), Université Paris-Saclay, Faculté de Médecine Paris-Saclay, Kremlin Bicêtre 94270, France.
Brain : a journal of neurology
|January 28, 2025
概括
计算神经精神病学使用主动推理 (AI) 解释精神病理. 这个框架表明,大脑疾病源于异常的信念更新,但推断和大脑功能障碍之间的区别挑战了当前的模型.
科学领域:
- 计算神经精神病学计算神经精神病学
- 神经科学是一个神经科学.
- 精神病学是一个精神病学.
背景情况:
- 计算神经精神病学通过神经网络和信念传播来解释精神病理.
- 主动推理 (AI) 通过最小化变化的自由能量来模拟功能失调的信号处理.
- 目前的人工智能方法将异常信念更新与神经精神症状联系起来.
研究的目的:
- 批判性地评估主动推理对神经精神和神经系统疾病全谱的适用性.
- 解决因推断障碍 (突触) 和大脑功能障碍 (断开) 之间的区别所带来的挑战.
- 为神经精神病症状提出一个改进的建模框架.
主要方法:
- 对主动推理原理的审查和概念分析.
- 检查突触病和断开障碍之间的区别.
- 这些概念的理论整合用于未来的建模.
主要成果:
- 积极推理通过专注于神经元信息传递和信念更新,成功地模拟推理障碍.
- 在推断障碍 (如精神病) 和大脑功能障碍 (如神经病) 之间存在着明确的区别.
- 这种区别突出了仅仅基于神经元信息传递的自由能量原则的应用局限性.
结论:
- 未来的神经精神症状的计算模型必须纳入推断和脑功能障碍之间的区别.
- 考虑超出神经元信息传递的因素,如神经调节精度和结构断开,至关重要.
- 这种细微的方法将增强计算模型对更广泛的大脑疾病的解释能力.
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