神经和精神疾病中的因果模型和网络不稳定性:计算和临床观点
Charles Okanda Nyatega1, Qiang Li2, Weizhi Nie3
1School of Microelectronics, Tianjin University, Tianjin, China; Department of Electronics and Telecommunication Engineering, Mbeya University of Science and Technology, Mbeya, Tanzania.
Behavioural brain research
|December 25, 2025
概括
递归不稳定性,当自我监测失败时,会影响大脑,免疫系统和AI. 了解这个共同的原则可以导致适应性,人工智能驱动干预帕金森病和败血症等疾病.
科学领域:
- 综合神经科学是一个整体的神经科学.
- 计算精神病学是一种计算精神病学.
- 系统免疫学 系统免疫学
- 人工智能的人工智能是人工智能.
背景情况:
- 自我诊断,即系统检测和纠正自身故障的能力,对于适应性系统至关重要.
- 在大脑中,递归自我监测对于感知,行动和认知至关重要,但可以破坏网络的稳定.
- 在免疫衰退 (败血症) 和人工智能 (AI) 失败模式中也观察到类似的复发性不稳定性.
研究的目的:
- 展示因果模型和网络不稳定性的统一框架.
- 整合预测编码,计算精神病学,网络神经科学,免疫学和人工智能.
- 分析帕金森病和败血症作为案例研究,并将精神分裂症和双相情感障碍作为精神病学的平行.
主要方法:
- 在统一的框架内分析复发性故障.
- 将帕金森病,败血症,精神分裂症和双相情感障碍放在一个共同的不稳定空间内.
- 将预测编码与跨生物领域的网络级动态联系起来.
主要成果:
- 递归失败表现为刚性先验,放大错误或跨条件的振荡吸引力.
- 递归不稳定性源于先验和预测错误之间的不平衡精度,破坏反循环的稳定性.
- 在神经系统,精神病学和免疫系统以及人工智能中确定了共享的漏洞和动态.
结论:
- 递归不稳定性是一种跨诊断原则,它将大脑,免疫系统和AI联系起来.
- 这种观点激励了国家依赖的生物标志物和自适应疗法的闭环干预.
- 建议使用人工智能增强的因果干预,将计算洞察与临床实践相结合.
关键词:
人工智能的人工智能是人工智能.双极性障碍是一种双极性障碍.因果模型是一种因果模型.网络神经科学 网络神经科学神经调节是一种神经调节.帕金森病是帕金森氏症的一种疾病.预测编码是指预测性的编码.递归的不稳定性精神分裂症是一种精神分裂症.败血症 这是一种败血症.更多相关视频
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