ArticlePLoS computational biology2022
Sleep prevents catastrophic forgetting in spiking neural networks by forming a joint synaptic weight representation.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 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
- Modular inhibitory coding in binary networks.Frontiers in network physiology · 2026Article
- Hippocampal indexing alters the stability landscape of synaptic weight space allowing life-long learning.bioRxiv : the preprint server for biology · 2025Article
- Cholinergic modulation of neural networks supports sequential and complementary roles for NREM and REM states in memory consolidation.PLoS computational biology · 2025Article
- REM refines and rescues memory representations: a new theory.Sleep advances : a journal of the Sleep Research Society · 2025Review
- Python/NEURON code for simulating biophysically realistic thalamocortical dynamics during sleep.Software impacts · 2024Article
- Detecting Information Relays in Deep Neural Networks.Entropy (Basel, Switzerland) · 2023Article
- Sleep-like unsupervised replay reduces catastrophic forgetting in artificial neural networks.Nature communications · 2022Article
- Training a spiking neuronal network model of visual-motor cortex to play a virtual racket-ball game using reinforcement learning.PloS one · 2022Article
- Replay in Deep Learning: Current Approaches and Missing Biological Elements.Neural computation · 2021Article
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Authors and funding
4 authors.
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Abstract
Artificial neural networks overwrite previously learned tasks when trained sequentially, a phenomenon known as catastrophic forgetting. In contrast, the brain learns continuously, and typically learns best when new training is interleaved with periods of sleep for memory consolidation. Here we used spiking network to study mechanisms behind catastrophic forgetting and the role of sleep in preventing it. The network could be trained to learn a complex foraging task but exhibited catastrophic forgetting when trained sequentially on different tasks. In synaptic weight space, new task training moved the synaptic weight configuration away from the manifold representing old task leading to forgetting. Interleaving new task training with periods of off-line reactivation, mimicking biological sleep, mitigated catastrophic forgetting by constraining the network synaptic weight state to the previously learned manifold, while allowing the weight configuration to converge towards the intersection of the manifolds representing old and new tasks. The study reveals a possible strategy of synaptic weights dynamics the brain applies during sleep to prevent forgetting and optimize learning.
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