ArticleScientific reports2020
Brain experiments imply adaptation mechanisms which outperform common AI learning algorithms.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 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
5 citing papers in PubMed.
- Enhancing the accuracies by performing pooling decisions adjacent to the output layer.Scientific reports · 2023Article
- Efficient shallow learning as an alternative to deep learning.Scientific reports · 2023Article
- Learning on tree architectures outperforms a convolutional feedforward network.Scientific reports · 2023Article
- Toward Systems Models for Obesity Prevention: A Big Role for Big Data.Current developments in nutrition · 2022Article
- Power-law scaling to assist with key challenges in artificial intelligence.Scientific reports · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Attempting to imitate the brain's functionalities, researchers have bridged between neuroscience and artificial intelligence for decades; however, experimental neuroscience has not directly advanced the field of machine learning (ML). Here, using neuronal cultures, we demonstrate that increased training frequency accelerates the neuronal adaptation processes. This mechanism was implemented on artificial neural networks, where a local learning step-size increases for coherent consecutive learning steps, and tested on a simple dataset of handwritten digits, MNIST. Based on our on-line learning results with a few handwriting examples, success rates for brain-inspired algorithms substantially outperform the commonly used ML algorithms. We speculate this emerging bridge from slow brain function to ML will promote ultrafast decision making under limited examples, which is the reality in many aspects of human activity, robotic control, and network optimization.
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