Electricity and light each have strengths and weaknesses as communications media. This means, inevitably, that the two must be used together.
Researchers from Carlos III de Madrid University in Spain and the Massachusetts Institute of Technology have devised a neural network architecture that uses a different mix of optics and electronics than previous schemes in order to accommodate large numbers of neurons. The architecture leverages the computational strength of electronics and the fast communications abilities of light. It could be useful in systems that require optical input and neural net computation -- like those used for robotic vision.
Neural networks, like the human brain, have many interconnected elements. Neural networks learn by assigning weights to the connections between neurons and changing the weights based on use. This allows a specific set of inputs to be associated with a pattern of weighted neural connections.
The researchers' system carries out the neural weights assignment electronically, which cuts down on the number of difficult-to-align optical connections compared to previous optoelectronic neural networks, according to the researchers.
The design makes it possible to scale the system to a very high
number of elements, or neurons, and the systemís optical interconnects
allow for fast communications among neurons.
The device can be used in practical applications in two to five
years, according to the researchers. The work appeared in the September,
2003 issue of Optical Engineering.
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