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arXiv Machine Learning By Arnol Manuel Fokam, Fasseu Sieyondji Akpevwoghene, Edem Fiifi Dawson

How Temporal Correlations Shape Memory in Linear Recurrent Neural Networks

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The paper analyzes how temporal correlations in input sequences affect memory formation in linear recurrent neural networks (LRNNs). By solving the learning dynamics for correlated inputs, it shows that correlation introduces a cost to retaining past information, reshaping the learning trajectory and reducing the network’s memory of past inputs. Key findings include a correlation‑dependent threshold for memory retention, the influence of input similarity on memory usefulness, and the emergence of a feedthrough path when zero error is required.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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