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Blurry image processes with computer model developed by the researchers  (Source: Center for the Neural Basis of Cognition, Pittsburgh, Pa)
Researchers prove that neurons in the olfactory bulb are not fixed and slow responding but change on the fly to stimuli

Scientists have traditionally believed that the way our brains process smells via neuron connections in the olfactory bulb was dictated by the anatomy of the olfactory bulb and could only change slowly in response to stimulus.

A new study from researchers at the Center for the Neural Basis of Cognition (CNBC), which is a joint project between Carnegie Mellon University and the University of Pittsburgh, has described a mechanism called dynamic connectivity.

Associate professor of biological sciences at Carnegie Mellon Nathan Urban describes the process like this, “If you think of the brain like a computer, then the connections between neurons are like the software that the brain is running. Our work shows that this biological software is changed rapidly as a function of the kind of input that the system receives.”

The researchers believe this ability to change neuronal circuits on the fly depending on the input is the reason we are able to walk into a room and notice a floral scent, then determine that it is certainly a floral smell and then narrow it down to the smell of roses.

To prove that the neurons do behave as the scientists predicted; a computer-modeling program was used to simulate the effects of stimuli on slices of olfactory bulb from a mouse viewed under a microscope with a water immersion objective at 20x, 40x, or 60x. The slices of mouse olfactory bulb were excited with specific excitation wavelengths in the 480 to 520nm range.  The process was also videoed with a special camera.

The researchers then created a continuous firing rate network model in MATLAB that represented a 25 x 25 array of simulated non-spiking neurons representing olfactory bulb mitral cells. The simulated cells active firing rate was represented by a continuous variable.

Using this process, Urban and other researchers on the project were able to show that lateral inhibition is enhanced by dynamic connectivity when a large number of neurons respond to a stimulus and filter out the noise from other neurons. This separation of noise from other neurons allows stimuli to be more clearly recognized and separated from other similar stimuli.





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