Brain connectivity can build better AI
Artificial neural networks modeled on real brains can perform cognitive
tasks

Date:
August 9, 2021
Source:
McGill University
Summary:
By examining MRI data from a large Open Science repository,
researchers reconstructed a brain connectivity pattern, and applied
it to an artificial neural network (ANN). They trained the ANN
to perform a cognitive memory task and observed how it worked to
complete the assignment. These 'neuromorphic' neural networks
were able to use the same underlying architecture to support a
wide range of learning capacities across multiple contexts.



FULL STORY ==========================================================================
A new study shows that artificial intelligence networks based on human
brain connectivity can perform cognitive tasks efficiently.


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By examining MRI data from a large Open Science repository, researchers reconstructed a brain connectivity pattern, and applied it to an
artificial neural network (ANN). An ANN is a computing system consisting
of multiple input and output units, much like the biological brain. A team
of researchers from The Neuro (Montreal Neurological Institute-Hospital)
and the Quebec Artificial Intelligence Institute trained the ANN to
perform a cognitive memory task and observed how it worked to complete
the assignment.

This is a unique approach in two ways. Previous work on brain
connectivity, also known as connectomics, focused on describing brain organization, without looking at how it actually performs computations
and functions. Secondly, traditional ANNs have arbitrary structures that
do not reflect how real brain networks are organized.By integrating brain connectomics into the construction of ANN architectures, researchers hoped
to both learn how the wiring of the brain supports specific cognitive
skills, and to derive novel design principles for artificial networks.

They found that ANNs with human brain connectivity, known as neuromorphic neural networks, performed cognitive memory tasks more flexibly and
efficiently than other benchmark architectures. The neuromorphic neural networks were able to use the same underlying architecture to support
a wide range of learning capacities across multiple contexts.

"The project unifies two vibrant and fast-paced scientificdisciplines,"
says Bratislav Misic, a researcher at The Neuro and the paper's senior
author.

"Neuroscience and AI share common roots, but have recently diverged. Using artificial networks will help us to understand how brain structure
supports brain function. In turn, using empirical data to make neural
networks will reveal design principles for building better AI. So,
the two will help inform each other and enrich our understanding
of the brain." This study, published in the journal Nature Machine Intelligence on Aug. 9, 2021, was funded with the help of the Canada
First Research Excellence Fund, awarded to McGill University for the
Healthy Brains, Healthy Lives initiative, the Natural Sciences and
Engineering Research Council of Canada, Fonds de Recherche du Quebec --
Sante', Canadian Institute for Advanced Research, Canada Research Chairs,
Fonds de Recherche du Quebec -- Nature et Technologies, and Centre UNIQUE (Union of Neuroscience and Artificial Intelligence).

========================================================================== Story Source: Materials provided by McGill_University. Note: Content
may be edited for style and length.


========================================================================== Journal Reference:
1. Laura E. Sua'rez, Blake A. Richards, Guillaume Lajoie, Bratislav
Misic.

Learning function from structure in neuromorphic networks. Nature
Machine Intelligence, 2021; DOI: 10.1038/s42256-021-00376-1 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/08/210809144041.htm

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