The `surprisingly simple' arithmetic of smell
Adding and subtracting certain neurons tells researchers whether or not a locust can smell an odor

Date:
January 10, 2022
Source:
Washington University in St. Louis
Summary:
Researchers studying locusts have found that the presence of smell
can be determined by simply adding and subtracting the presence
of certain neurons.



FULL STORY ========================================================================== Smell a cup of coffee.


========================================================================== Smell it inside or outside; summer or winter; in a coffee shop with a
scone; in a pizza parlor with pepperoni -- even at a pizza parlor with
a scone! -- coffee smells like coffee.

Why don't other smells or different environmental factors "get
in the way," so to speak, of the experience of smelling individual
odors? Researchers at the McKelvey School of Engineering at Washington University in St. Louis turned to their trusted research subject, the
locust, to find out.

What they found was "surprisingly simple," according to Barani Raman,
professor of biomedical engineering. Their results were published in
the journal Proceedings of the National Academy of Sciences.

Raman and colleagues have been working with locusts for years, watching
their brains and their behaviors related to smell in an attempt to
engineer bomb- sniffing locusts. Along the way, they've made substantial
gains when it comes to understanding the mechanisms at play when it
comes to locusts' sense of smell.

To understand how it is that a locust can consistently recognize
smells regardless of context, they took a cue from Ivan Pavlov. Like
Pavlov's dogs, locusts were trained to associate an odor with food,
their preference being a blade of grass. After going a day without
food, a locust was exposed to a puff of odor (a puff of hexanol or
isoamyl acetate), then given a blade of grass. In as few as six such presentations, the locust learned to open its palps (sensory appendages
close to the mouth) in expectation of a snack after simply smelling the "training odorant." Just like us recognizing coffee, the trained locust
could recognize the odor and did not let other factors get in the way.



==========================================================================
At this point, researchers began looking at which neurons were firing
when the locust was exposed to the odor under different conditions,
including in conjunction with other smells, in humid or dry conditions,
when they were starved or fully fed, trained or untrained, and for
different amounts of time.

Under different circumstances, it turned out, researchers saw highly inconsistent patterns of neurons were activated even though the locust
palps opened every time. "The neural responses were highly variable,"
Raman said.

"That seemed to be at odds with what the locusts were doing,
behaviorally." How could variable neural responses produce consistent or stable behavior? To probe this, researchers turned to a machine-learning algorithm. "We wanted to see if given these variable neural response
patterns, can we predict the locust behavior?" Raman said. "The answer was
yes, we can." The algorithm turned out to be very simple to interpret. It exploited two functional types of neurons: there are ON neurons, which
are activated when an odorant is present, and there are OFF neurons,
which are silenced when an odorant is present but become activated after
the odor presentation ends.

"You can think of the ON neurons as conveying 'evidence for' an odor being present, and OFF neurons as 'evidence against' that odor being present,"
Raman said. To recognize an odorant's presence, researchers simply needed
to add evidence for the odorant being present (i.e. add the spikes across
all ON neurons) and subtract evidence against that odor being present
(i.e. add the spikes across all OFF neurons). If the result was above
a certain threshold, machine learning would predict the locust smelled
the odor.

"We were surprised to find that this simple approach is all that was
needed to robustly recognize an odorant," Raman said.

Raman likened the process to shopping for a shirt. Say you have a list
of qualities you're looking for -- cotton, long sleeves, button-down,
solid color, maybe a front pocket to hold your glasses -- and a few dealbreakers, such as dry-clean only or polka dots.

You may get lucky and find a shirt that is precisely what you are
looking for.

But, more pragmatically, you would make a purchase as long as many of
the features you are looking for are present and the majority of features
that are deal breakers are not present.

Finding the features you want is similar to the information conveyed by
the ON neurons. Absence of deal breakers is similar to silencing of the
OFF neurons.

As long as enough ON neurons that are typically activated by an odorant
have fired -- and most OFF neurons have not -- it would be a safe bet
to predict that the locust will open its palps in anticipation of a
grassy treat.

special promotion Explore the latest scientific research on sleep and
dreams in this free online course from New Scientist -- Sign_up_now_>>> academy.newscientist.com/courses/science-of-sleep-and-dreams ========================================================================== Story Source: Materials provided by
Washington_University_in_St._Louis. Original written by Brandie
Jefferson. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Srinath Nizampatnam, Lijun Zhang, Rishabh Chandak, James Li,
Baranidharan
Raman. Invariant odor recognition with ON-OFF neural ensembles.

Proceedings of the National Academy of Sciences, 2022; 119 (2):
e2023340118 DOI: 10.1073/pnas.2023340118 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/01/220110145300.htm
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