# Slumbering to Precision: Enhancing Artificial Neural Network Calibration Through Sleep-like Processes Source: [Slumbering to Precision: Enhancing Artificial Neural Network Calibration Through Sleep-like Processes]() Collection index: [llms.txt]() Data updated: 2026-03-11T05:00:11.798Z Authors: Jean Erik Delanois, Aditya Ahuja, Giri P. Krishnan, Maxim Bazhenov Published: 2026-03-09T00:43:14.000Z ## Abstract Artificial neural networks are often overconfident, undermining trust because their predicted probabilities do not match actual accuracy. Inspired by biological sleep and the role of spontaneous replay in memory and learning, we introduce Sleep Replay Consolidation (SRC), a novel calibration approach. SRC is a post-training, sleep-like phase that selectively replays internal representations to update network weights and improve calibration without supervised retraining. Across multiple experiments, SRC is competitive with and complementary to standard approaches such as temperature scaling. Combining SRC with temperature scaling achieves the best Brier score and entropy trade-offs for AlexNet and VGG19. These results show that SRC provides a fundamentally novel approach to improving neural network calibration. SRC-based calibration offers a practical path toward more trustworthy confidence estimates and narrows the gap between human-like uncertainty handling and modern deep networks. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on CIFAR-100 (Calibration Evaluation)](): Image Classification; CIFAR-100; Accuracy: 84.8. ## Original sources and implementation [Original paper]() [PDF]()