如果不能很快忘記，或忽視應該忽視的事物,，計算機也會精神分裂,。據(jù)美國物理學家組織網(wǎng)5月6日（北京時間）報道，最近,，德克薩斯大學和耶魯大學一個聯(lián)合研究小組用計算機模擬的“神經(jīng)網(wǎng)絡”來模仿大腦中多巴胺的過度釋放,，發(fā)現(xiàn)網(wǎng)絡中重現(xiàn)的記憶和精神分裂中的幻覺非常相似，為人們進一步理解精神分裂患者大腦的內(nèi)部機制提供了線索,。相關論文發(fā)表在近期的《生物精神病學》上,。
目前一種“過度學習”假說認為，精神分裂患者失去了遺忘的能力,，或不能忽視本應忽視的東西,。無法遺忘，也就喪失了從大量的腦刺激信號中提取分辨出含義的能力,。他們開始制造不真實的聯(lián)系,，淹沒在海洋般的聯(lián)系之網(wǎng)中,，卻沒能力梳理出任何相關的故事。而多巴胺太多,，會導致大腦不能忽視那些它不必知道的事物,。
德克薩斯大學奧斯汀分校計算機科學系研究生烏利·格里斯曼與導師里斯托·秘庫賴恩設計了稱為“明辨”的神經(jīng)網(wǎng)絡，能在實驗中模擬出8種不同類型神經(jīng)機能障礙的語言反應,，他們與耶魯大學醫(yī)學院從事人類精神病學研究的拉爾夫·霍夫曼教授合作,，將計算機模擬結果與人類精神疾病進行了對比。
他們先教給“明辨”一系列簡單的故事,，以人腦儲存信息的方式把這些故事輸入“明辨”的記憶,，這種存儲方式不是把故事作為獨立單元，而是按照字,、詞,、句和故事的統(tǒng)計相關性。然后一遍一遍地示范,，訓練“明辨”在不同指令下提取記憶,，輸出不同的故事。“這么做幾千次,，每次調(diào)整一小點兒作為進步,，最后神經(jīng)網(wǎng)絡就會學會。”格里斯曼說,。
隨后他們加入了系統(tǒng)學習速度參數(shù),，模擬多巴胺的過度釋放，基本上就是讓計算機不再遺忘,。“大腦的一種重要機制是忽視事物,，如果讓‘明辨’學習速度太快，就會出現(xiàn)反常語言,，正像精神分裂患者那樣,。”格里斯曼說。
重新訓練之后,，“明辨”開始出現(xiàn)幻覺妄想,，從其存儲的其他故事中提取元素，組合起來編造故事,。比如有一次,，它聲稱自己對一起恐怖爆炸事件負責。而另一次,，被要求用一堆毫不相關的句子回答某個記憶時,，它突然跑題，不斷重復前三個句子,。
格里斯曼表示,，“明辨”神經(jīng)網(wǎng)絡的信息處理方式和人腦很相似,，因此也可能像人腦那樣崩潰掉。計算機模擬試驗雖不能證明過度學習假說完全正確,，卻明顯支持這一假說,。而計算機模擬的神經(jīng)網(wǎng)絡更易控制，這類研究有望為精神類病患找到合適的臨床療法,。（生物谷Bioon.com）
生物谷推薦原文出處：
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.
