研究揭示人類和小鼠推理背後的神經元計算
作者:
小柯機器人發布時間:2020/9/22 14:20:17
英國牛津大學David Dupret、Helen C. Barron等研究人員合作揭示人類和小鼠推理背後的神經元計算。相關論文於2020年9月17日在線發表於國際學術期刊《細胞》。
研究人員在人類和小鼠中使用了一種跨物種方法來報告推理決策所依據的功能解剖和神經元計算。研究人員顯示,在成功的推理過程中,哺乳動物的大腦使用海馬前瞻性代碼來預測時間結構化的學習聯想。此外,在休息行為期間,尖波/漣漪中海馬細胞的共激活代表推斷的關係,其中包括獎勵,從而在未一起觀察到的事件之間產生「聯繫」,但帶來了有益的結果。以這種方式計算記憶聯繫可以提供一種重要的機制,從而建立超越直接體驗的認知圖譜來支持靈活的行為方式。
據介紹,人類每天做出的決策對於適應和生存至關重要。人類重複行動會產生已知的後果。但是,人類也利用不大相關的事件來推斷和想像全新選擇的結果。這些推論性決策被認為涉及許多大腦區域。然而,潛在的神經元計算仍然未知。
附:英文原文
Title: Neuronal Computation Underlying Inferential Reasoning in Humans and Mice
Author: Helen C. Barron, Hayley M. Reeve, Renée S. Koolschijn, Pavel V. Perestenko, Anna Shpektor, Hamed Nili, Roman Rothaermel, Natalia Campo-Urriza, Jill X. O』Reilly, David M. Bannerman, Timothy E.J. Behrens, David Dupret
Issue&Volume: 2020-09-17
Abstract: Every day we make decisions critical for adaptation and survival. We repeat actions with known consequences. But we also draw on loosely related events to infer and imagine the outcome of entirely novel choices. These inferential decisions are thought to engage a number of brain regions; however, the underlying neuronal computation remains unknown. Here, we use a multi-day cross-species approach in humans and mice to report the functional anatomy and neuronal computation underlying inferential decisions. We show that during successful inference, the mammalian brain uses a hippocampal prospective code to forecast temporally structured learned associations. Moreover, during resting behavior, coactivation of hippocampal cells in sharp-wave/ripples represent inferred relationships that include reward, thereby 「joining-the-dots」 between events that have not been observed together but lead to profitable outcomes. Computing mnemonic links in this manner may provide an important mechanism to build a cognitive map that stretches beyond direct experience, thus supporting flexible behavior.
DOI: 10.1016/j.cell.2020.08.035
Source: https://www.cell.com/cell/fulltext/S0092-8674(20)31077-1
Cell:《細胞》,創刊於1974年。隸屬於細胞出版社,最新IF:36.216