Neurons
battle to a draw
By
Kimberly Patch,
Technology Research NewsLocusts -- those flying grasshoppers that periodically cause farmers distress by collecting into swarms and binging on whole fields of crops -- learn a lot about their environments from the olfactory information gathered by their antennas.
A group of scientists who are trying to understand exactly how smells picked up by Locust antennas are translated into neural signals have taken a step toward understanding how neurons can store as much information as they do.
Nature's advantage comes from the way neurons interact. In their winnerless competition, no neuron gains an advantage over any other, which keeps the system as a whole coordinated in the face of random signals, or noise.
The work promises to increase by several orders of magnitude the amount of information that artificial neural networks can work with, and the potential number of smells artificial noses can distinguish. Artificial neural networks underpin computer vision systems that recognize objects and faces and pattern recognition software that sorts large amounts of scientific and financial data.
Neurons work together in much the same way that a small number of letters can form many different words, said Mikhail Rabinovich, a research scientist at the University of California at San Diego. Rather than one or a group of neurons simply representing a single smell, a group of neurons can represent many smells depending on the order in which they're fired, he said. "The sequences of the activity of these neurons is important."
Label three neurons blue, red and green. Instead of each neuron representing a different smell for a total of three possible smells, they can work together to represent 12 different smells. Neurons firing in the order blue, red, green, for instance, could represent a rose smell, and the same neurons fired in another sequence could represent a salmon smell, Rabinovich said. Here's the math for three digit combinations of three separate letters or neurons: 3 x 2 x 1 = 6, and then x 2 again to take into account the combinations that can be represented by repeating digits = 12.
The true advantage of such a system becomes apparent when you do the math for larger strings of letters or neurons. As small a number as 10 different neurons can represent 7.25 million unique 10-digit combinations. (Here's the math: 10 x 9 x 8 x 7 x 6 x 5 x 4 x 3 x 2 x 2 = 7.25 million.)
By using the timing, or order in which neurons switch, the neural network gains "a huge capacity," Rabinovich said.
The system is also robust, meaning it cannot be easily thrown off, because this structure is inherently self-correcting, said Rabinovich. Neurons are constantly switching, or oscillating on and off at about 1 hertz, or cycle per second. Random signals, or noise can throw the system off, but because the system inherently dissipates the random signals, it stays on track.
Neural networks are able to do this through stimulus-dependent winnerless competition, said Rabinovich.
The same winnerless competition principle works to balance animal populations that are competing for a territory or food. For example, if species A eats species B, species B eats species C, and species C eats species A, the three populations tend to stay balanced. This is because if species B gets ahead in eating species A, there will be higher numbers of species C around to pare down species B, which will in turn allow species A to rebound.
Neurons work the same way because when a neuron fires, it can inhibit the firing of another neuron. This sets up a competition among neurons, which works to keep their timing coordinated in a way that can represent useful information, said Rabinovich. Though the principle of winnerless competition is not new, it is new to show that such dynamics can represent the sensory input that causes neurons to cycle, he said.
Eventually scientists can use this information to build better artificial neural networks, whose abilities have historically fallen far short of those of the biological kind. "Maybe we can use this idea to build artificial [systems] that demonstrate the same abilities -- robustness against noise, huge capacity, reproduceability and sensitivity," said Rabinovich.
One possibility is building an olfactory sensor -- an artificial smart nose "that is able to represent and recognize a huge number of different stimuli," he said. It can also be used to "organize brains which are able to control the behavior of robots in complex environments," said Rabinovich.
The work is "very impressive," said Sylvian Ray, a professor of computer science and electrical and computer engineering at the University of Illinois. "It appears to add another dimension to neural network architectures, which potentially permit a huge increase in the number of states... representable by a modest size collection of neurons," said Ray. What's new are the specific equations to describe the kinetics, and the correlation between the mathematical model and the neural activity in the Locust antenna lobe, he said.
The work may help solve the mystery of why biological neural networks can do so much more than artificial networks that try to copy their structures, Ray added. "There is a possibility that this idea is a clue to the way in which biological networks can represent such an astounding number of states per neuron," he said.
It will be a least five years before the work can be applied practically, said Rabinovich.
Rabinovich's research colleagues were Aleksandr Volkovskii from the University of California at San Diego, P. Lecanda from the Madrid Autonoma University in Spain and the Madrid Institute of Materials Science in Spain, Ramon Huerta from UC San Diego and the Madrid Autonona University, Henry Abarbanel from UC San Diego and the Scripps Institution of Oceanography, and Gilles Laurent from the California Institute of Technology.
They published the research in the August 6, 2001 issue of the journal Physical Review Letters. The research was funded by the Department of Energy (DOE), the National Science Foundation (NSF) and the National Institutes of Health (NIH).
Timeline: 5 years
Funding: Government
TRN Categories: Neural Networks
Story Type: News
Related Elements: Technical paper, "Dynamical Encoding by Networks of Competing Neuron Groups: Winnerless Competition," Physical Review Letters, August 6, 2001.
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October 3, 2001
Page One
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