Insect Senses Suggest Novel Neural Networks

Animals gather information about their environments when sensory neurons fire minute electrical signals in response to chemicals, light, sounds, and other stimuli. Studying networks of neurons in animals and insects can provide us with insight to the natural world as well as inspiration for manmade networks to aid in computing and other applications.

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 (M. Rabinovich, mrabinovich@ucsd.edu, 858-534-6753) propose that connections between neurons can cause one neuron to delay the firing of another neuron. As a result, a given stimulus leads to a specific time sequence of neural impulses. In essence, the interconnected neurons include time as another dimension of sensory systems through an encoding method called Winnerless Competition (WLC).

Using a locust antenna lobe exposed to fragrances such as cherry and mint for comparison, the researchers found their model could identify roughly (N-1)! (equal to (N-1) x (N-2) x ...x 2) items with a network built of N neurons. That is, a ten neuron WLC network should be able to identify hundreds of thousands as many items as a conventional ten-neuron network, and the benefits increase as networks grow.

The WLC model helps explain how the senses of animals, insects, and even humans can accurately and robustly distinguish between so many stimuli. In other words, it is a mathematical rationale as to why a rose, by any other name, would smell as sweet---but doesn't smell like an onion. 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; text at Physics News Select).