Brain experiments imply adaptation mechanisms which outperform common AI learning algorithms

Shira Sardi, Roni Vardi, Yuval Meir, Yael Tugendhaft, Shiri Hodassman, Amir Goldental, Ido Kanter

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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.

Original languageEnglish
Article number6923
JournalScientific Reports
Issue number1
StatePublished - 23 Apr 2020

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© 2020, The Author(s).


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