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原文連結
論文資訊
- 類型：已發表論文
- 日期：2020-12-22
摘要
A central challenge in the 計算 modeling of 神經 dynamics is the trade-off between accuracy and simplicity. At the level of individual neurons, 非線性 dynamics are both experimentally established and essential for neuronal functioning. One may therefore expect the collective dynamics of massive 網絡s of such neurons to exhibit an even larger repertoire of 非線性 behaviors. An implicit assumption has thus formed that an “accurate” 計算 model of whole-大腦 dynamics must inevitably be non-linear whereas linear models may provide a first-order approximation. To what extent this assumption holds, however, has remained an open question. Here, we provide new evidence that challenges this assumption at the level of whole-大腦 blood-oxygen-level-dependent (BOLD) and macroscopic field potential dynamics by leveraging
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