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Experimental observations conclude learning is mainly performed by neural dendrite trees as opposed to modifying solely through the strength of the synapses, as previously believed.
The brain is a complex network containing billions of neurons. Each of these neurons communicates simultaneou...
For the last 70 years a core hypothesis of neuroscience has been that brain learning occurs by modifying the strength of the synapses, following the relative firing activity of connecting neurons.
This hypothesis has been the basis for machine and deep learning algorithms which increasingly...
The new experimental observations suggest that learning is mainly performed in neuronal dendritic trees, where the trunk and branches of the tree modify their strength, as opposed to modifying solely the strength of the synapses (dendritic leaves), as was previously thought.
“We’ve shown that efficient learning on dendritic trees of a single neuron can artificially achieve success rates approaching unity for handwritten digit recognition. This finding paves the way for an efficient biologically inspired new type of AI hardware and algorithms,” said Prof. Ido...
“This simplified learning mechanism represents a step towards a plausible biological realization of backpropagation algorithms, which are currently the central technique in AI,” added Shiri Hodassman, a Ph.D. student and one of the key contributors to this work.
The efficient learn...
The brain’s clock is a billion times slower than existing parallel GPUs, but with comparable success rates in many perceptual tasks.
The new demonstration of efficient learning on dendritic trees calls for new approaches in brain research, as well as for the generation of counterpart hardwa...
About this neuroscience and learning research news
Author: Press Office
Source: Bar-Ilan University
Contact: Press Office – Bar-Ilan University
Image: The image is credited to Bar-Ilan University
Original Research: Ope...
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