Washington, March 9 : American scientists have come up with a new way to view the world through the eyes of a common fly, and to partially decode the insect's reactions to changes in the world around it.
Los Alamos physicist Ilya Nemenman says that the new research fundamentally alters earlier beliefs about how neural networks function.
The researcher also believes that the new findings may provide the basis for intelligent computers that mimic biological processes.
The research team - including Geoffrey Lewen of the Hun School of Princeton, William Bialek of Princeton University, and Rob de Ruyter van Steveninck of Indiana University - used tiny electrodes to tap into motion-sensitive neurons in the visual system of a common blowfly.
In their study report, published in the Public Library of Science Computational Biology Journal, the researchers have described neurons as nerve cells that emit tiny electric spikes when stimulated.
They said that the electrodes detected pulses from the motion-sensitive neurons in the fly, which it uses to estimate, and subsequently control its movement through the world.
The team harnessed the wired fly into an elaborate turntable-like mechanism that mimics the kind of acrobatic flight a fly might undergo while evading a predator or chasing another fly, for it can spin extremely fast and change velocities quickly.
A fly in the mechanism sees changes in the world around it, and its motion-sensitive neurons react much in the same way as they would if the insect were actually flying.
The researchers observed that under complex flight scenarios, the fly's neurons fired very quickly.
Mapping the firing patterns with a binary code of ones and zeroes, they found that the impulses were like a primitive, but very regular "language", with the neuron firing at precise times depending on what the fly's visual sensors were trying to tell the rest of the fly about the visual stimulus.
The examination of the language spoke volumes about how the harnessed fly reacted to its world.
"Biological organisms have an interest in conserving energy. Fly eyes account for about one-tenth of the fly's energy consumption. The fly wants to be very efficient, but it costs energy and molecular resources to emit many precise spikes in the neurons," Nemenman said.
"If you are presenting simple stimuli where little changes with time, then the most efficient way to encode them may be to generate few randomly positioned spikes, which would be sufficient to convey whatever small changes, if any, happened. Similarly, if the stimulus is unnaturally fast, the neurons may not be able to encode it well.
"However, if you put an organism in an environment with fast and naturally changing velocity profiles, the fly starts using all the bandwidth available to it. The motion-sensitive neuron adjusts its coding strategy and it uses the precise positioning of the spikes to tell the rest of the fly exactly what is happening," Nemenman added.
Nemenman and his colleagues' research is significant because it re-examines fundamental assumptions that became the basis of neuromimetic approaches to artificial intelligence, such as artificial neural networks. Such assumptions have developed networks based on reacting to a number of impulses within a given time period rather than the precise timing of the impulses.
"This may be one of the main reasons why artificial neural networks do not perform anywhere comparable to a mammalian visual brain. In fact, the National Science Foundation has recognized the importance of this distinction and has recently funded a project, led by Garrett Kenyon of the Laboratory's Physics Division, to enable creation of large, next-generation neural networks," said Nemenman.
The researchers believe that new understanding of neural function in the design of computers may assist in analysing satellite images, recognising facial-pattern in high-security environments, and solving other national and global security problems.