ReviewAmerican journal of neurodegenerative disease2024
Neural reshaping: the plasticity of human brain and artificial intelligence in the learning process.
Review in American journal of neurodegenerative disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Neural parasitism: could adaptive artificial intelligence systems incrementally reconfigure human neural plasticity and challenge the foundations of cognitive autonomy?Annals of medicine and surgery (2012) · 2026Article
- Neuromorphic algorithms for brain implants: a review.Frontiers in neuroscience · 2025Review
- The Evolution of Vision Therapy Software and Its Impact on Vision Care - A Comprehensive Major Review.Romanian journal of ophthalmologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study explores the concept of neural reshaping and the mechanisms through which both human and artificial intelligence adapt and learn.
objectivesTo investigate the parallels and distinctions between human brain plasticity and artificial neural network plasticity, with a focus on their learning processes.
methodsA comparative analysis was conducted using literature reviews and machine learning experiments, specifically employing a multi-layer perceptron neural network to examine regression and classification problems.
resultsExperimental findings demonstrate that machine learning models, similar to human neuroplasticity, enhance performance through iterative learning and optimization, drawing parallels in strengthening and adjusting connections.
conclusionsUnderstanding the shared principles and limitations of neural and artificial plasticity can drive advancements in AI design and cognitive neuroscience, paving the way for future interdisciplinary innovations.
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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.