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原文連結
論文資訊
- 類型：已發表論文
- 日期：2021-04-19
摘要
The ability to store and manipulate 資訊 is a hallmark of 計算 systems. Whereas computers are carefully engineered to represent and perform 數學 operations on structured data, neuro生物 systems adapt to perform analogous functions without needing to be explicitly engineered. Recent efforts have made progress in modelling the representation and recall of 資訊 in 神經 systems. However, precisely how 神經 systems learn to modify these representations remains far from understood. Here, we demonstrate that a recurrent 神經 網絡 (RNN) can learn to modify its representation of complex 資訊 using only examples, and we explain the associated learning mechanism with new theory. Specifically, we drive an RNN with examples of translated, linearly transformed or pre-bifurcated time series from a chaotic Lorenz system, alo
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