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 simultaneously with thousands of others via their synapses (links), and collects incoming signals through several extremely long, branched “arms,” called dendritic trees.Source: Bar-Ilan University

New Neuron New Learning

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 affect most of all our lives. After decades, this long-lasting hypothesis has now been called into question.In an article published today in Scientific Reports, researchers from Bar-Ilan University in Israel reveal that the brain learns completely differently than has been assumed since the 20th century.

How The Brain Learns 

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.These observations indicate that the neuron is a much more complex, dynamic and computational element than a binary element that can fire or not.Just one single neuron can realize deep learning algorithms, which previously required an artificial complex network consisting of thousands of connected neurons and synapses.

Neuronal Dendritic Trees

“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 Kanter, of Bar-Ilan’s Department of Physics and Gonda (Goldschmied) Multidisciplinary Brain Research Center, who led the research.

Prof. Ido Kanter

“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 learning on dendritic trees is based on Kanter and his research team’s experimental evidence for sub-dendritic adaptation using neuronal cultures, together with other anisotropic properties of neurons, like different spike waveforms, refractory periods and maximal transmission rates.

Simplified Learning Mechanism

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 hardware aiming to implement advanced AI algorithms. If one can implement slow brain dynamics on ultrafast computers, the sky is the limit.

The Sky is the Limit.

About this neuroscience and learning research newsAuthor: Press OfficeSource: Bar-Ilan UniversityContact: Press Office – Bar-Ilan UniversityImage: The image is credited to Bar-Ilan UniversityOriginal Research: Open access.“Efficient dendritic learning as an alternative to synaptic plasticity hypothesis” by Shiri Hodassman et al. Scientific Report

 Bar-Ilan University

Summary: 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.Source: Bar-Ilan UniversityThe brain is a complex network containing billions of neurons. Each of these neurons communicates simultaneously with thousands of others via their synapses (links), and collects incoming signals through several extremely long, branched “arms,” called dendritic trees.For the last 70 years a core hypothesis of neuroscience has been that&nbsp;brain&nbsp;learning occurs by modifying the strength of the synapses, following the relative firing activity of their connecting neurons.This hypothesis has been the basis for machine and deep learning algorithms which increasingly affect almost all aspects of our lives. But after seven decades, this long-lasting hypothesis has now been called into question.In an article published today in&nbsp;Scientific Reports, researchers from Bar-Ilan University in Israel reveal that the brain learns completely differently than has been assumed since the 20th century.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.These observations also indicate that the neuron is actually a much more complex, dynamic and computational element than a binary element that can fire or not.Just one single neuron can realize&nbsp;deep learning algorithms, which previously required an artificial&nbsp;complex network&nbsp;consisting of thousands of connected neurons and synapses.“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 Kanter, of Bar-Ilan’s Department of Physics and Gonda (Goldschmied) Multidisciplinary Brain Research Center, who led the research.<figure><img width="725" height="731" alt="This shows jigsaw pieces" srcset="https://neurosciencenews.com/files/2022/04/new-learning-hypothesis-neurosciences-.jpg 770w, https://neurosciencenews.com/files/2022/04/new-learning-hypothesis-neurosciences--298x300.jpg 298w, https://neurosciencenews.com/files/2022/04/new-learning-hypothesis-neurosciences--100x100.jpg 100w, https://neurosciencenews.com/files/2022/04/new-learning-hypothesis-neurosciences--293x295.jpg 293w, https://neurosciencenews.com/files/2022/04/new-learning-hypothesis-neurosciences--96x96.jpg 96w, https://neurosciencenews.com/files/2022/04/new-learning-hypothesis-neurosciences--150x151.jpg 150w" src="https://neurosciencenews.com/files/2022/04/new-learning-hypothesis-neurosciences-.jpg"><figcaption dir="auto">A paradigm shift in brain research: The new neuron and the new type of learning. Credit: Prof. Ido Kanter, Bar-Ilan University</figcaption></figure>“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 learning on dendritic trees is based on Kanter and his research team’s experimental evidence for sub-dendritic adaptation using neuronal cultures, together with other anisotropic properties of&nbsp;neurons, like different spike waveforms, refractory periods and maximal transmission rates.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&nbsp;brain research, as well as for the generation of counterpart hardware aiming to implement advanced AI algorithms. If one can implement slow brain dynamics on ultrafast computers, the sky is the limit.<h2>About this neuroscience and learning research news</h2>Author: <a href="http://www1.biu.ac.il/indexE.php" rel="noreferrer noopener" dir="ltr">Press Office</a> Source: <a href="http://www1.biu.ac.il/indexE.php" rel="noreferrer noopener" dir="ltr">Bar-Ilan University</a> Contact: Press Office – Bar-Ilan University Image: The image is credited to Bar-Ilan UniversityOriginal Research: Open access. “<a href="https://www.nature.com/articles/s41598-022-10466-8" rel="noreferrer noopener" dir="ltr">Efficient dendritic learning as an alternative to synaptic plasticity hypothesis</a>” by Shiri Hodassman et al. Scientific ReportsAbstract<div>See also</div>Efficient dendritic learning as an alternative to synaptic plasticity hypothesisSynaptic plasticity is a long-lasting core hypothesis of brain learning that suggests local adaptation between two connecting neurons and forms the foundation of machine learning.The main complexity of synaptic plasticity is that synapses and dendrites connect neurons in series and existing experiments cannot pinpoint the significant imprinted adaptation location.We showed efficient backpropagation and Hebbian learning on dendritic trees, inspired by experimental-based evidence, for sub-dendritic adaptation and its nonlinear amplification.It has proven to achieve success rates approaching unity for handwritten digits recognition, indicating realization of deep learning even by a single dendrite or neuron.Additionally, dendritic amplification practically generates an exponential number of input crosses, higher-order interactions, with the number of inputs, which enhance success rates.However, direct implementation of a large number of the cross weights and their exhaustive manipulation independently is beyond existing and anticipated computational power.Hence, a new type of nonlinear adaptive dendritic hardware for imitating dendritic learning and estimating the computational capability of the brain must be built.<div>Join our Newsletter</div><div dir="ltr"> I agree to have my personal information transferred to AWeber for Neuroscience Newsletter ( <a href="https://www.aweber.com/privacy.htm" dir="ltr">more information</a> )</div><div>Sign up to receive our recent neuroscience headlines and summaries sent to your email once a day, totally free.</div><div>We hate spam and only use your email to contact you about newsletters. You can cancel your subscription any time.</div><div>Tags</div>

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 organised mind

Summary: 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.

Source: Bar-Ilan University

The brain is a complex network containing billions of neurons. Each of these neurons communicates simultaneously with thousands of others via their synapses (links), and collects incoming signals through several extremely long, branched “arms,” called dendritic trees.

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 their connecting neurons.

This hypothesis has been the basis for machine and deep learning algorithms which increasingly affect almost all aspects of our lives. But after seven decades, this long-lasting hypothesis has now been called into question.

In an article published today in Scientific Reports , researchers from Bar-Ilan University in Israel reveal that the brain learns completely differently than has been assumed since the 20th century.

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.

These observations also indicate that the neuron is actually a much more complex, dynamic and computational element than a binary element that can fire or not.

Just one single neuron can realize deep learning algorithms, which previously required an artificial complex network consisting of thousands of connected neurons and synapses.

“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 Kanter, of Bar-Ilan’s Department of Physics and Gonda (Goldschmied) Multidisciplinary Brain Research Center, who led the research.

“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 learning on dendritic trees is based on Kanter and his research team’s experimental evidence for sub-dendritic adaptation using neuronal cultures, together with other anisotropic properties of neurons, like different spike waveforms, refractory periods and maximal transmission rates.

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 hardware aiming to implement advanced AI algorithms. If one can implement slow brain dynamics on ultrafast computers, the sky is the limit.

About this neuroscience and learning research news

Author: <a href="http://www1.biu.ac.il/indexE.php">Press Office </a> Source: <a href="http://www1.biu.ac.il/indexE.php">Bar-Ilan University </a> Contact: Press Office – Bar-Ilan University Image: The image is credited to Bar-Ilan University

Original Research: Open access. “<a href="https://www.nature.com/articles/s41598-022-10466-8">Efficient dendritic learning as an alternative to synaptic plasticity hypothesis </a>” by Shiri Hodassman et al. Scientific Reports

Efficient dendritic learning as an alternative to synaptic plasticity hypothesis

Synaptic plasticity is a long-lasting core hypothesis of brain learning that suggests local adaptation between two connecting neurons and forms the foundation of machine learning.

The main complexity of synaptic plasticity is that synapses and dendrites connect neurons in series and existing experiments cannot pinpoint the significant imprinted adaptation location.

We showed efficient backpropagation and Hebbian learning on dendritic trees, inspired by experimental-based evidence, for sub-dendritic adaptation and its nonlinear amplification.

It has proven to achieve success rates approaching unity for handwritten digits recognition, indicating realization of deep learning even by a single dendrite or neuron.

Additionally, dendritic amplification practically generates an exponential number of input crosses, higher-order interactions, with the number of inputs, which enhance success rates.

However, direct implementation of a large number of the cross weights and their exhaustive manipulation independently is beyond existing and anticipated computational power.

Hence, a new type of nonlinear adaptive dendritic hardware for imitating dendritic learning and estimating the computational capability of the brain must be built.

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New Brain Learning Mechanism Calls for Revision of Long-Held Neuroscience Hypothesis - Neuroscience News

New Neuron New Learning

How The Brain Learns

Neuronal Dendritic Trees

Prof. Ido Kanter

Simplified Learning Mechanism

The Sky is the Limit.

Bar-Ilan University

IDEAS ABOUT

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How Neuroplasticity Changes the Brain

What is Neuroplasticity? A Psychologist Explains

How the Brain Develops | Psychology Today

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New Brain Learning Mechanism Calls for Revision of Long-Held Neuroscience Hypothesis - Neuroscience News

New Neuron New Learning

How The Brain Learns

Neuronal Dendritic Trees

Prof. Ido Kanter

Simplified Learning Mechanism

The Sky is the Limit.

Bar-Ilan University

IDEAS ABOUT

MORE LIKE THIS

How Neuroplasticity Changes the Brain

What is Neuroplasticity? A Psychologist Explains

How the Brain Develops | Psychology Today

It's time to

READ

LIKE

A PRO!

Bite-sized knowledge
to upgrade
your career