ArticleeLife2020
Energy efficient synaptic plasticity.
Article in eLife, 2020. 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.
- 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
- Striatal Gradient in Value-Decay Explains Regional Differences in Dopamine Patterns and Reinforcement Learning Computations.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- A systematic review on type 3 diabetes: bridging the gap between metabolic dysfunction and Alzheimer's disease.Diabetology & metabolic syndrome · 2025Review
- Characterizing a highly excited and sustained brain response activity during gaming: P300-CE.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Ecosystems as adaptive living circuits.bioRxiv : the preprint server for biology · 2025Article
- Synaptic weight dynamics underlying memory consolidation: Implications for learning rules, circuit organization, and circuit function.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Reinforcement learning when your life depends on it: A neuro-economic theory of learning.PLoS computational biology · 2024Article
- Competitive plasticity to reduce the energetic costs of learning.PLoS computational biology · 2024Article
- Activity-driven synaptic translocation of LGI1 controls excitatory neurotransmission.Cell reports · 2024Article
- Brain energy metabolism: A roadmap for future research.Journal of neurochemistry · 2024Review
- How can caching explain automaticity?Psychonomic bulletin & review · 2023Review
- Small, correlated changes in synaptic connectivity may facilitate rapid motor learning.Nature communications · 2022Article
- Norepinephrine potentiates and serotonin depresses visual cortical responses by transforming eligibility traces.Nature communications · 2022Article
- Learning induces coordinated neuronal plasticity of metabolic demands and functional brain networks.Communications biology · 2022Article
- The AI trilemma: Saving the planet without ruining our jobs.Frontiers in artificial intelligence · 2022Article
- Postsynaptic Potential Energy as Determinant of Synaptic Plasticity.Frontiers in computational neuroscience · 2022Article
- Energy constraints on brain network formation.Scientific reports · 2021Article
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Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Many aspects of the brain's design can be understood as the result of evolutionary drive toward metabolic efficiency. In addition to the energetic costs of neural computation and transmission, experimental evidence indicates that synaptic plasticity is metabolically demanding as well. As synaptic plasticity is crucial for learning, we examine how these metabolic costs enter in learning. We find that when synaptic plasticity rules are naively implemented, training neural networks requires extremely large amounts of energy when storing many patterns. We propose that this is avoided by precisely balancing labile forms of synaptic plasticity with more stable forms. This algorithm, termed synaptic caching, boosts energy efficiency manifold and can be used with any plasticity rule, including back-propagation. Our results yield a novel interpretation of the multiple forms of neural synaptic plasticity observed experimentally, including synaptic tagging and capture phenomena. Furthermore, our results are relevant for energy efficient neuromorphic designs.
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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.