A new model of neural networks, based on recent studies of fish and insect olfactory systems, suggests a way that neurons can be linked together to allow them to identify many more stimuli than possible with conventional networks. Researchers from the Institute for Nonlinear Science at the University of California, San Diego propose that connections between neurons can cause one neuron to delay the firing of another neuron system. The interconnected neurons include time as another dimension of sensory systems through an encoding method called Winnerless Competition (WLC).

The researchers found their model could identify roughly factorial (N-1) items with a network built of N neurons. For example, a ten-neuron WLC network should be able to identify hundreds of thousands as many items as a conventional ten-neuron network.

The WLC model helps explain how the senses of animals, insects, and even humans can accurately and robustly distinguish between so many stimuli.

Ultimately, the WLC model may lead to high capacity, potent computing networks that resemble an insect antenna or a human nose more than a desktop PC.

(M. Rabinovich et al, Physical Review Letters, 6 August 2001)