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and judgments, and hence,
many areas of the brain. One
restaurant has good food and
service, another, so-so. One
Neuroscientist Maps Decision- has higher prices, another
is cheaper. Experience
Making To Aid Understanding provides the inputs that
must be assigned values and
Of The Brain considered for the decision
to be made.
With trial and error, repetition and praise, when a “It’s very difficult to
puppy hears “Sit!” they learn what they’re expected to do. integrate all of these processes, and yet, somehow, our
That’s reinforcement learning, and it’s a complex subject Hattori focuses on understanding and mapping brains do that,” Hattori says.
that fascinates neuroscientist Ryoma Hattori, Ph.D., who reinforcement learning and how the brain integrates Understanding the mechanisms that underlie this
recently joined The Herbert Wertheim UF Scripps Institute information to make decisions. He also studies how the process may prove important in addressing psychiatric
for Biomedical Innovation & Technology. brain comprehends numbers. What seems simple on its and autism spectrum disorders, he notes.
Ryoma Hattori, Ph.D., uses both advanced laboratory face is actually stunningly complex. The human brain has “Many psychiatric diseases and neurological disorders
techniques, including virtual reality and 2-photon approximately 86 billion neurons, which make more than feature some impairment in decision-making,” he says.
imaging, plus AI to make discoveries about how the 100 trillion connections. Modeling how multiple brain areas interact to process
brain learns and makes decisions. He recently joined The Hattori says many factors play into the decision- reinforcing experiences and guide decision-making is
Wertheim UF Scripps Institute for Biomedical Innovation making process. Something as simple as deciding an interesting challenge, he says. Hattori uses many
& Technology in Jupiter, Fla. where to eat may involve a matrix of memories research techniques to gather data, including large-scale
2-photon imaging, virtual reality-based experiments,
and optogenetics, a method for using light to manipulate
neural activity. Computational modeling is increasingly
a valuable tool to understand complex animal behaviors
and brain dynamics, Hattori says.
Hattori and colleagues are developing artificial
intelligence to assist with their research. It’s a two-
way relationship: AI helps advance the neuroscience
discoveries, and the neuroscience discoveries may also
help improve the AI.
“Both the brain and AI are made of neural networks
that perform computations and learn using neural activity
dynamics and synaptic plasticity,” Hattori says. “They
receive external inputs, process the information and
output an action. Then, the outcome of the action guides
the learning by the network. The similarity gives us an
opportunity to use AI as a neural network model for
certain behaviors.”
Hattori recently moved to The Wertheim UF Scripps
campus in Jupiter, Fla., following a postdoctoral
fellowship at the University of California, San Diego. He
earned his doctorate in molecular and cellular biology at
Harvard University in 2016.
An assistant professor in the institute’s neuroscience
department, he’s also a recipient of many awards,
including the Warren Alpert Distinguished Scholar
award and the Simons Foundation SFARI Bridge-to-
Independence award.
His wife is a neuroscientist as well, Mariko Hattori,
Ph.D. She recently joined the lab of Kirill Martemyanov,
Ph.D., chair of the neuroscience department, as a
postdoctoral researcher. The Hattoris have a 15-month-
old son, and enjoy taking him to the ocean when they’re
not in their labs.
The Jupiter community has become a great magnet
for neuroscientists, they said. The Wertheim UF Scripps’
strong program is joined by the neighboring Florida
Atlantic University Stiles-Nicholson Brain Institute and
the Max Planck Florida Institute for Neuroscience.
The Hattoris collaborated with Max Planck’s scientific
director, Ryohei Yasuda, Ph.D., on a recently published
Nature Neuroscience paper about the role of a brain
region called the orbitofrontal cortex in the acquisition
of generalized knowledge.
The scientists found multiple layers of learning at work
in mouse adaptation to new environments, with different
time scales. The mouse learning mechanisms resembled
those of a computer model of reinforcement learning that
was developed by AI researchers.
“We can gain insights into brain mechanisms from AI.
Also, as we better understand the brain mechanisms for
decision-making and learning, we may be able to transfer
the knowledge to AI models,” Ryoma Hattori says. “I
hope my research projects contribute to understanding of
the brain, and also contribute to development of AI with
better performance in the machine learning community
as well.”
April 22
