ArticleNeural computation2021
Replay in Deep Learning: Current Approaches and Missing Biological Elements.
Article in Neural computation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 99 citations in OpenAlex.
- Invertebrate brains as diversified models for understanding sleep mechanisms and function.Journal of comparative physiology. A, Neuroethology, sensory, neural, and behavioral physiology · 2026Review
- Article
- The Role of Plasticity in Replay: Stability Through Anti-Hebbian Rules.Hippocampus · 2026Article
- Hippocampal indexing alters the stability landscape of synaptic weight space allowing life-long learning.bioRxiv : the preprint server for biology · 2025Article
- Plasticity in inhibitory networks improves pattern separation in early olfactory processing.Communications biology · 2025Article
- Article
- Elements of episodic memory: insights from artificial agents.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Review
- Paradoxical replay can protect contextual task representations from destructive interference when experience is unbalanced.bioRxiv : the preprint server for biology · 2024Article
- Continual Deep Learning for Time Series Modeling.Sensors (Basel, Switzerland) · 2023Review
- Online Continual Learning in Acoustic Scene Classification: An Empirical Study.Sensors (Basel, Switzerland) · 2023Article
- Neuro-inspired continual anthropomorphic grasping.iScience · 2023Article
- Large Language Models and the Reverse Turing Test.Neural computation · 2023Article
- Privacy-preserving continual learning methods for medical image classification: a comparative analysis.Frontiers in medicine · 2023Article
- Sleep-like unsupervised replay reduces catastrophic forgetting in artificial neural networks.Nature communications · 2022Article
- A neural network account of memory replay and knowledge consolidation.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity.PLoS computational biology · 2022Article
- Sleep prevents catastrophic forgetting in spiking neural networks by forming a joint synaptic weight representation.PLoS computational biology · 2022Article
- A model of autonomous interactions between hippocampus and neocortex driving sleep-dependent memory consolidation.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Stochastic consolidation of lifelong memory.Scientific reports · 2022Article
- Training spiking neuronal networks to perform motor control using reinforcement and evolutionary learning.Frontiers in computational neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
Replay is the reactivation of one or more neural patterns that are similar to the activation patterns experienced during past waking experiences. Replay was first observed in biological neural networks during sleep, and it is now thought to play a critical role in memory formation, retrieval, and consolidation. Replay-like mechanisms have been incorporated in deep artificial neural networks that learn over time to avoid catastrophic forgetting of previous knowledge. Replay algorithms have been successfully used in a wide range of deep learning methods within supervised, unsupervised, and reinforcement learning paradigms. In this letter, we provide the first comprehensive comparison between replay in the mammalian brain and replay in artificial neural networks. We identify multiple aspects of biological replay that are missing in deep learning systems and hypothesize how they could be used to improve artificial neural networks.
Indexed as
Identifiers
What OpenQuestion holds
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.