ArticleNature communications2022
Sleep-like unsupervised replay reduces catastrophic forgetting in artificial neural networks.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
18 citing papers in PubMed.
- Invertebrate brains as diversified models for understanding sleep mechanisms and function.Journal of comparative physiology. A, Neuroethology, sensory, neural, and behavioral physiology · 2026Review
- Neural rhythms as priors of speech computations.Patterns (New York, N.Y.) · 2026Article
- Two-factor synaptic plasticity enables memory consolidation during neuronal burst firing.PNAS nexus · 2026Article
- The Role of Plasticity in Replay: Stability Through Anti-Hebbian Rules.Hippocampus · 2026Article
- Reinforcement learning improves LLM accuracy and reasoning in disease classification from radiology reports.NPJ digital medicine · 2026Article
- Advances and Perspectives in Graphene-Based Quantum Dots Enabled Neuromorphic Devices.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Hippocampal indexing alters the stability landscape of synaptic weight space allowing life-long learning.bioRxiv : the preprint server for biology · 2025Article
- Two-factor synaptic consolidation reconciles robustness with pruning and homeostatic scaling.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Anterograde interference in multitask perceptual learning.NPJ science of learning · 2025Article
- A programmable and self-adaptive ultrasonic wireless implant for personalized chronic pain management.Nature electronics · 2025Article
- The Missing Half of Language Learning in Current Developmental Language Models: Exogenous and Endogenous Linguistic Input.Open mind : discoveries in cognitive science · 2025Article
- Neural circuits and emotional processing in rapid eye movement sleep.Frontiers in psychiatry · 2025Review
- Closing the gap between open source and commercial large language models for medical evidence summarization.NPJ digital medicine · 2024Article
- Closing the gap between open-source and commercial large language models for medical evidence summarization.ArXiv · 2024Article
- Article
- Data driven of underground water level using artificial intelligence hybrid algorithms.Scientific reports · 2023Article
- Sleep loss diminishes hippocampal reactivation and replay.Research square · 2023Article
- Stimulation Augments Spike Sequence Replay and Memory Consolidation during Slow-Wave Sleep.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Artificial neural networks are known to suffer from catastrophic forgetting: when learning multiple tasks sequentially, they perform well on the most recent task at the expense of previously learned tasks. In the brain, sleep is known to play an important role in incremental learning by replaying recent and old conflicting memory traces. Here we tested the hypothesis that implementing a sleep-like phase in artificial neural networks can protect old memories during new training and alleviate catastrophic forgetting. Sleep was implemented as off-line training with local unsupervised Hebbian plasticity rules and noisy input. In an incremental learning framework, sleep was able to recover old tasks that were otherwise forgotten. Previously learned memories were replayed spontaneously during sleep, forming unique representations for each class of inputs. Representational sparseness and neuronal activity corresponding to the old tasks increased while new task related activity decreased. The study suggests that spontaneous replay simulating sleep-like dynamics can alleviate catastrophic forgetting in artificial neural networks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.