Fiber-optic 'photonic neurons' to be developed in collaboration between Princeton University and Lockheed Martin

Princeton, NJ and Cherry Hill, NJ--Researchers at Princeton University and Lockheed Martin are collaborating on a "photonic neuron" project, with the aim of creating an optical-fiber-based computational device that works similarly to neurons except that it will be faster by a factor of a billion. The properties of neurons—such as thresholding—are nonlinear, which will have to be imitated optically, for example by using nonlinear fiber.

Practical uses of a neuron-type photonic circuit include those that require quick decisions to be made from incomplete data, such as locating a terrorist from a radio signal or deciding whether to eject a fighter pilot from a jet. Other uses might include guiding robotic automobiles based on video input, or quickly gathering useful information from large amounts of genetic data.

The research is led by Paul Prucnal, a Princeton professor of electrical engineering, and David Rosenbluth, a neuroscientist and principle engineer at Lockheed Martin's Advance Technology Laboratory). It is funded by Lockheed Martin and Princeton's Stuart M. Essig and Erin S. Enright Fund for Innovation in Engineering and Neuroscience.

Initiated in 2008, the project seeks to overcome the inherent speed constraints of electrical circuits, which are ultimately limited by the time it takes electricity to flow through wires. Instead, the processing will be all-optical.

In addition, the hardware will rely on computational concepts used by neural circuits. In living neural networks, each neuron, whether in the brain or peripheral circuits of the nervous system, is connected to other neurons, which communicate through electrochemical pulses known as action potentials, or, colloquially, as "spikes." Based on the pattern of incoming spikes, a neuron decides whether to send out its own signal to convey information to the rest of the network. This function is the basis for neural computing.

Digital-analog hybrid

"We are transposing learning, inhibition, and other behaviors typical of neural processing onto fiber-optic circuits," Rosenbluth said. "But I don't think of it as trying to reproduce something in the brain. It's a hybrid between the analog computing done in the brain and the purely digital systems used by most computers."

When Prucnal and Rosenbluth first began talking about the possibility of studying blending fiber-optic signal processing and neuroscience, they noticed that although the mathematical equations used to model neural and fiber-optic networks used different variables, they were very similar in their overall formulation.

"We put the equations side-by-side and it was really an 'aha!' moment for us," Prucnal said. "It was pretty exciting that it might actually work."

(Source: http://www.princeton.edu/main/news/archive/S31/07/36I49/index.xml?section=featured)