EMBARGO: April 2nd, 2015, 11am Pacific/2pm Eastern/7pm UK
Modular Brains Help Organisms Learn New Skills without Forgetting Old Skills
Video summary: https://youtu.be/6PlvyWQUQu8
New research suggests that when brains are organized into modules they are better at learning new
information without forgetting old knowledge. The findings—published this week in PLOS
Computational Biology—not only shed light on the evolution of intelligence in natural animals, but will
also accelerate attempts to create
artificial intelligence (AI).
Kai Olav Ellefsen (Norwegian
University of Science and
Technology), Jean-Baptiste
Mouret (Pierre & Marie Curie
University) and Jeff Clune
(University of Wyoming) used
simulations of evolving
computational brain models called
artificial neural networks to show
that more modular brains learn
more and forget less.
The brains of animals (including humans) are modular, which means they have many separate units,
such as those for language and facial recognition. While natural animals tend to forget gradually,
artificial neural networks currently exhibit what is called ‘catastrophic forgetting’. They rapidly
overwrite previously acquired knowledge when learning a new skill. The researchers found that
modularity significantly reduced such catastrophic
forgetting in these computer brains.
In future work, the researchers plan to dramatically scale
up the complexity of the brain models and the difficulty of
the tasks they ask the neural networks to learn. “Building
models that incorporate both evolution and learning is
critical to understanding the evolution of the animal nervous
system”, Jean-Baptiste Mouret says. Jeff Clune adds: “The
ultimate goal of artificial intelligence research is to produce
AI that can learn many different skills and get better at each
of them over time, just as humans and animals do. We
must solve the problem of catastrophic forgetting to realize
that goal. This work is an important step in that direction,
but it is just one step in a long journey.”
Image 1 Credit: Thinking robot: Pixabay user DrSJS; Chess player: Jeffrey Barke; Pelé playing
soccer: Wikimedia Commons, by AFP/SCANPI.
Image 2 Credit: Dreamstime LLC
All works published in PLOS Computational Biology are Open Access, which means that all content is
immediately and freely available. Use this URL in your coverage to provide readers access to the
paper upon publication: http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004128
Press-only preview: http://www.plos.org/wp-content/uploads/2015/03/pcbi.10041281.pdf
Contact: Jeff Clune
Address: University of Wyoming
Computer Science
1000 E. University
Laramie, WY 82070
UNITED STATES
Phone: 5172141060
Email: jeffclune@uwyo.edu
Citation: Ellefsen KO, Mouret J-B, Clune J (2015) Neural Modularity Helps Organisms Evolve to
Learn New Skills without Forgetting Old Skills. PLoS Comput Biol 11(4): e1004128.
doi:10.1371/journal.pcbi.1004128
Funding: KOE and JC have no specific financial support for this work. JBM is supported by an ANR
young researchers grant (Creadapt, ANR-12-JS03-0009). URL: http://www.agence-nationalerecherche.fr/en/funding-opportunities/documents/aap-en/generic-call-for-proposals-2015-2015/nc/.
The funders had no role in study design, data collection and analysis, decision to publish, or
preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
About PLOS Computational Biology
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further our understanding of living systems at all scales through the application of computational
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