Learning to evolve: With a little help from my ancestors

According to a new theory by Dr Stone of Sheffield University, skills such as flying are easy to refine because the innate ability of today's birds depends indirectly on the learning that their ancestors did, which leaves a genetically specified latent memory for flying.

The theory has been tested on simple models of brains called artificial neural networks, which can be made to evolve using genetic algorithms.

Whilst these networks do not fly, they do learn associations, and these associations could take the form of a skill such as flying. Using computer simulations, Stone demonstrates in a study, publishing in the open access journal PLoS Computational Biology, that the ability to learn in network models has two surprising consequences.

First, learning accelerates the rate at which a skill becomes innate over generations, so it accelerates the evolution of innate skill acquisition. For comparison, evolution is slow if a network simply inherits its innate ability from its parents, but is not allowed to learn in order to improve this innate ability. Second, learning in previous generations indirectly induces the formation of a latent memory in the current generation, and therefore decreases the amount of learning required. It matters how quickly learning occurs, because time spent learning is time spent not eating, or time spent being eaten, which incurs the ultimate penalty for slow learners. These effects are especially pronounced if there is a large biological 'fitness cost' to learning, where biological fitness is measured in terms of the number of offspring each individual has.

Crucially, the beneficial effects of learning depend on the unusual form of information storage in neural networks, a form common to biological and artificial neural networks. Unlike computers, which store each item of information in a specific location in the computer's memory chip, neural networks store each item distributed over many neuronal connections. If information is stored as distributed representations then evolution is accelerated. This may help explain how complex motor skills, such as nest building and hunting skills, are acquired by a combination of innate ability and learning over many generations.

The new theory has its roots in ideas proposed by James Baldwin in 1896, who made the counter-intuitive argument that learning within each generation could guide evolution of innate behaviour over future generations. It now seems that Baldwin may have been more right than he could have guessed, even though concepts such as artificial neural networks and distributed representations were not known in his time.

A previous version of this article appeared as an Early Online Release on June 8, 2007 (doi: 10.1371/journal.pcbi.0030147.eor).

Media Contact

Andrew Hyde alfa

More Information:

http://www.ploscompbiol.org

All latest news from the category: Life Sciences and Chemistry

Articles and reports from the Life Sciences and chemistry area deal with applied and basic research into modern biology, chemistry and human medicine.

Valuable information can be found on a range of life sciences fields including bacteriology, biochemistry, bionics, bioinformatics, biophysics, biotechnology, genetics, geobotany, human biology, marine biology, microbiology, molecular biology, cellular biology, zoology, bioinorganic chemistry, microchemistry and environmental chemistry.

Back to home

Comments (0)

Write a comment

Newest articles

Lighting up the future

New multidisciplinary research from the University of St Andrews could lead to more efficient televisions, computer screens and lighting. Researchers at the Organic Semiconductor Centre in the School of Physics and…

Researchers crack sugarcane’s complex genetic code

Sweet success: Scientists created a highly accurate reference genome for one of the most important modern crops and found a rare example of how genes confer disease resistance in plants….

Evolution of the most powerful ocean current on Earth

The Antarctic Circumpolar Current plays an important part in global overturning circulation, the exchange of heat and CO2 between the ocean and atmosphere, and the stability of Antarctica’s ice sheets….

Partners & Sponsors