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原文連結
論文資訊
- 類型：已發表論文
- 日期：2001
摘要
We describe a mechanism for 生物 learning and 適應 based on two simple principles: (i) Neuronal activity propagates only through the 網絡's strongest synaptic connections (extremal dynamics), and (ii) the strengths of active synapses are reduced if mistakes are made, otherwise no changes occur (negative feedback). The balancing of those two tendencies typically shapes a synaptic landscape with configurations which are barely stable, and therefore highly flexible. This allows for swift 適應 to new situations. Recollection of past successes is achieved by punishing synapses which have once participated in activity associated with successful outputs much less than neurons that have never been successful. Despite its simplicity, the model can readily learn to solve complicated 非線性 tasks, even in the p
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