Using machine learning, a computer model can teach itself to smell in just a
few minutes. When it does, researchers have found, it builds a neural
network that closely mimics the olfactory circuits that animal brains use to
process odors.
Animals from fruit flies to humans all use essentially the same strategy to
process olfactory information in the brain. But neuroscientists who trained
an artificial neural network to take on a simple odor classification task
were surprised to see it replicate biology's strategy so faithfully.
"The algorithm we use has no resemblance to the actual process of
evolution," says Guangyu Robert Yang, an associate investigator at MIT's
McGovern Institute for Brain Research, who led the work as a postdoc at
Columbia University. The similarities between the artificial and biological
systems suggest that the brain's olfactory network is optimally suited to
its task.
Yang and his collaborators, who reported their findings Oct. 6 in the
journal Neuron, say their artificial network will help researchers learn
more about the brain's olfactory circuits. The work also helps demonstrate
artificial neural networks' relevance to neuroscience. "By showing that we
can match the architecture [of the biological system] very precisely, I
think that gives more confidence that these neural networks can continue to
be useful tools for modeling the brain," says Yang, who is also an assistant
professor in MIT's departments of Brain and Cognitive Sciences and
Electrical Engineering and Computer Science.
Mapping natural olfactory circuits
For fruit flies, the organism in which the brain's olfactory circuitry has
been best mapped, smell begins in the antennae. Sensory neurons there, each
equipped with odor receptors specialized to detect specific scents,
transform the binding of odor molecules into electrical activity. When an
odor is detected, these neurons, which make up the first layer of the
olfactory network, signal to the second layer: a set of neurons that reside
in a part of the brain called the antennal lobe. In the antennal lobe,
sensory neurons that share the same receptor converge onto the same
second-layer neuron. "They're very choosy," Yang says. "They don't receive
any input from neurons expressing other receptors." Because it has fewer
neurons than the first layer, this part of the network is considered a
compression layer. These second-layer neurons, in turn, signal to a larger
set of neurons in the third layer. Puzzlingly, those connections appear to
be random.
For Yang, a computational neuroscientist, and Columbia University graduate
student Peter Yiliu Wang, this knowledge of the fly's olfactory system
represented a unique opportunity. Few parts of the brain have been mapped as
comprehensively, and that has made it difficult to evaluate how well certain
computational models represent the true architecture of neural circuits,
they say.
Building an artificial smell network
Neural networks, in which artificial neurons rewire themselves to perform
specific tasks, are computational tools inspired by the brain. They can be
trained to pick out patterns within complex datasets, making them valuable
for speech and image recognition and other forms of artificial intelligence.
There are hints that the neural networks that do this best replicate the
activity of the nervous system. But, says Wang, who is now a postdoc at
Stanford University, differently structured networks could generate similar
results, and neuroscientists still need to know whether artificial neural
networks reflect the actual structure of biological circuits. With
comprehensive anatomical data about fruit fly olfactory circuits, he says,
"We're able to ask this question: Can artificial neural networks truly be
used to study the brain?"
Collaborating closely with Columbia neuroscientists Richard Axel and Larry
Abbott, Yang and Wang constructed a network of artificial neurons comprising
an input layer, a compression layer, and an expansion layer—just like the
fruit fly olfactory system. They gave it the same number of neurons as the
fruit fly system, but no inherent structure: connections between neurons
would be rewired as the model learned to classify odors.
The scientists asked the network to assign data representing different odors
to categories, and to correctly categorize not just single odors, but also
mixtures of odors. This is something that the brain's olfactory system is
uniquely good at, Yang says. If you combine the scents of two different
apples, he explains, the brain still smells apple. In contrast, if two
photographs of cats are blended pixel by pixel, the brain no longer sees a
cat. This ability is just one feature of the brain's odor-processing
circuits, but captures the essence of the system, Yang says.
It took the artificial network only minutes to organize itself. The
structure that emerged was stunningly similar to that found in the fruit fly
brain. Each neuron in the compression layer received inputs from a
particular type of input neuron and connected, seemingly randomly, to
multiple neurons in the expansion layer. What's more, each neuron in the
expansion layer receives connections, on average, from six compression-layer
neurons—exactly as occurs in the fruit fly brain.
"It could have been one, it could have been 50. It could have been anywhere
in between," Yang says. "Biology finds six, and our network finds about six
as well." Evolution found this organization through random mutation and
natural selection; the artificial network found it through standard machine
learning algorithms.
The surprising convergence provides strong support that the brain circuits
that interpret olfactory information are optimally organized for their task,
he says. Now, researchers can use the model to further explore that
structure, exploring how the network evolves under different conditions and
manipulating the circuitry in ways that cannot be done experimentally.
Reference:
Peter Y. Wang et al, Evolving the olfactory system with machine learning,
Neuron (2021).
DOI: 10.1016/j.neuron.2021.09.010