Artificial brains learn to adapt

Duke University researchers are studying a new type of spiking neural network that more closely mimics the behavioral learning processes of mammalian brains. Behavioral learning relies on sensory feedback to improve motor performance and enable people to quickly adapt to their changing environment. Spiking neural networks model brain dynamics, with neurons signaling to other neurons with a rapid spike in cell voltage. “Although existing engineering systems are very effective at controlling dynamics, they are not yet capable of handling unpredicted damages and failures handled by biological brains,” says Duke professor Silvia Ferrari, who is leading the research. The team is applying the spiking neural network model to complex engineering systems, such as aircraft and power plants. To accomplish this, the researchers wrote an algorithm that tells the spiking neural networks which information is relevant and ranks the importance of each factor to the main goal. The team now wants to test the algorithm biologically, using an optogenetics technique in which lab-grown brain cells are genetically altered to respond to certain types of light, enabling researchers to control how nerve cells communicate. The research could advance the development of prosthetic devices that fulfill motor, sensory, and cognitive functions.

More info: National Science Foundation (05/15/14)

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