image: 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. view more
Credit: Dreamstime LLC
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."
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Video summary: https://youtu.be/6PlvyWQUQu8
Image Credit: Thinking robot: Pixabay user DrSJS; Chess player: Jeffrey Barke; Pelé playing soccer: Wikimedia Commons, by AFP/SCANPI.
Image Link: http://www.plos.org/wp-content/uploads/2015/03/robot_two_skills.png
Image Credit: Dreamstime LLC
Image Link: http://www.plos.org/wp-content/uploads/2015/03/brainToNeuronZoom-HighRes.jpg
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
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-nationale-recherche.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.
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PLOS Computational Biology