研究用计算机模拟精神分裂形成机理

如果不能很快忘记，或忽视应该忽视的事物，计算机也会精神分裂。据美国物理学家组织网5月6日（北京时间）报道，最近，德克萨斯大学和耶鲁大学一个联合研究小组用计算机模拟的“神经网络”来模仿大脑中多巴胺的过度释放，发现网络中重现的记忆和精神分裂中的幻觉非常相似，为人们进一步理解精神分裂患者大脑的内部机制提供了线索。相关论文发表在近期的《生物精神病学》上。

目前一种“过度学习”假说认为，精神分裂患者失去了遗忘的能力，或不能忽视本应忽视的东西。无法遗忘，也就丧失了从大量的脑刺激信号中提取分辨出含义的能力。他们开始制造不真实的联系，淹没在海洋般的联系之网中，却没能力梳理出任何相关的故事。而多巴胺太多，会导致大脑不能忽视那些它不必知道的事物。

德克萨斯大学奥斯汀分校计算机科学系研究生乌利·格里斯曼与导师里斯托·秘库赖恩设计了称为“明辨”的神经网络，能在实验中模拟出8种不同类型神经机能障碍的语言反应，他们与耶鲁大学医学院从事人类精神病学研究的拉尔夫·霍夫曼教授合作，将计算机模拟结果与人类精神疾病进行了对比。

他们先教给“明辨”一系列简单的故事，以人脑储存信息的方式把这些故事输入“明辨”的记忆，这种存储方式不是把故事作为独立单元，而是按照字、词、句和故事的统计相关性。然后一遍一遍地示范，训练“明辨”在不同指令下提取记忆，输出不同的故事。“这么做几千次，每次调整一小点儿作为进步，最后神经网络就会学会。”格里斯曼说。

随后他们加入了系统学习速度参数，模拟多巴胺的过度释放，基本上就是让计算机不再遗忘。“大脑的一种重要机制是忽视事物，如果让‘明辨’学习速度太快，就会出现反常语言，正像精神分裂患者那样。”格里斯曼说。

重新训练之后，“明辨”开始出现幻觉妄想，从其存储的其他故事中提取元素，组合起来编造故事。比如有一次，它声称自己对一起恐怖爆炸事件负责。而另一次，被要求用一堆毫不相关的句子回答某个记忆时，它突然跑题，不断重复前三个句子。

格里斯曼表示，“明辨”神经网络的信息处理方式和人脑很相似，因此也可能像人脑那样崩溃掉。计算机模拟试验虽不能证明过度学习假说完全正确，却明显支持这一假说。而计算机模拟的神经网络更易控制，这类研究有望为精神类病患找到合适的临床疗法.

推荐原文出处：

Biological Psychiatry    DOI:10.1016/j.biopsych.2010.12.036

Using Computational Patients to Evaluate Illness Mechanisms in Schizophrenia

Ralph E. Hoffman, Uli Grasemann, Ralitza Gueorguieva, Donald Quinlan, Douglas Lane, Risto Miikkulainen

Background Various malfunctions involving working memory, semantics, prediction error, and dopamine neuromodulation have been hypothesized to cause disorganized speech and delusions in schizophrenia. Computational models may provide insights into why some mechanisms are unlikely, suggest alternative mechanisms, and tie together explanations of seemingly disparate symptoms and experimental findings. Methods Eight corresponding illness mechanisms were simulated in DISCERN, an artificial neural network model of narrative understanding and recall. For this study, DISCERN learned sets of autobiographical and impersonal crime stories with associated emotion coding. In addition, 20 healthy control subjects and 37 patients with schizophrenia or schizoaffective disorder matched for age, gender, and parental education were studied using a delayed story recall task. A goodness-of-fit analysis was performed to determine the mechanism best reproducing narrative breakdown profiles generated by healthy control subjects and patients with schizophrenia. Evidence of delusion-like narratives was sought in simulations best matching the narrative breakdown profile of patients. Results All mechanisms were equivalent in matching the narrative breakdown profile of healthy control subjects. However, exaggerated prediction-error signaling during consolidation of episodic memories, termed hyperlearning, was statistically superior to other mechanisms in matching the narrative breakdown profile of patients. These simulations also systematically confused autobiographical agents with impersonal crime story agents to model fixed, self-referential delusions. Conclusions Findings suggest that exaggerated prediction-error signaling in schizophrenia intermingles and corrupts narrative memories when incorporated into long-term storage, thereby disrupting narrative language and producing fixed delusional narratives. If further validated by clinical studies, these computational patients could provide a platform. for developing and testing novel treatments.