Highlights
Honeybee Olfaction
Submitted by the Mathematical Biosciences Institute (MBI)
A honeybee may forage on 1,000s of flowers for nectar and pollen in its
lifetime. Scent is one of the primary means that it uses for identifying
rewarding flowers. How honeybees and other animals learn to associate
complex and variable scents with important events is still not well
understood. Honeybees are an excellent model system for studying olfaction
because their physiology and behavior has been the subject of much research
in the past 100 years. Currently, honeybees can be conditioned to associate
an odor stimulus with a food reward. After conditioning, honeybees can be
tested with many different odors, allowing researchers to identify
perceptual similarities among odor stimuli. Additionally, invertebrates are
excellent models for studying neurophysiology, and much is known about the
honeybee brain.
Recently, MBI postdoc, Geraldine Wright, and Ohio State University
professor, Brian Smith, have pioneered the use of multi-electrode
recordings in the honeybee brain during odor stimulation. This technique
provides high temporal resolution of the activity of many neurons
simultaneously. Using this technique in coordination with behavioral and
mathematical modeling studies, it is possible to test specific hypotheses
about the way in which the brain encodes information about odors.
At the Mathematical Biosciences Institute, biologists, Geraldine Wright and
Brian Smith, have teamed up with mathematicians, Alla Borisyuk and David
Terman, to develop mathematical models of the honeybee antennal lobe. The
initial work focused on the spatial aspect of odor representation in the
honeybee antennal lobe. Two types of models were used for hypothesis
testing: a more biophysically-detailed network of spiking cells, and a more
abstract model in the form of an integro-differential equation. The results
will now be used test specific hypotheses about the role of inhibition in
the honeybee antennal lobe using electrophysiology. The electrophysiology,
in turn, will drive improvement of the models.
Recently, these models have been extended to analyze the temporal component
of odor encoding (grant proposal submitted). The issue of whether the
temporal features contribute to odor-coding in the olfactory system is
hotly debated in the experimental community, and is a source of many
theoretical challenges. Our interdisciplinary team, centered at MBI, is
well suited for the task.
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