November 18, 2019
Cold Spring Harbor Laboratory
In learning new tasks, neuron
networks in the brain of mice become more refined and selective. Charting
changes in neural activity can help inform the design of better computational
models for understanding decision making and cognition.
When mice learn to do a new
task, their brain activities change over time as they advance from 'novice' to
'expert.' The changes are reflected in the wiring of cell circuits and
activities of neurons.
Using a two-photon imaging
microscope and a wealth of genetic tools, researchers from Cold Spring Harbor
Laboratory (CSHL), Columbia University, University College London, and Flatiron
Institute found that neural networks become more focused as mice got better at
performing a trained task. They used the data to construct computational models
that can inform their understanding of the neuroscience behind decision-making.
"We recorded the activity
from hundreds of neurons all at the same time, and studied what the neurons did
over learning," said CSHL Associate Professor Anne Churchland.
"Nobody really knew how animals or humans learn the structure of a task
and how the neural activity supports that."
The team, including Farzaneh
Najafi, the first author on the study and a postdoctoral fellow in Churchland's
lab, started by training mice on perceptual decision-making tasks. The mice
received multisensory stimuli in the form of a sequence of clicks and flashes
that were presented together. Their job was to tell researchers whether those
are happening at a high or low rate by licking one of three waterspouts in
front of them.
They licked the middle spout
to start the trial, one side to report a high-rate decision and the other side
for a low-rate decision. When the mice made the correct decision, they received
a reward.
"Most decision-making
studies focused on the period where the animals are really experts. But we were
able to see how they arrive at the state by measuring the neurons in their
brain all the way through learning," said Churchland, the senior author on
the study. "We found that in all the animals, their learning occurs
gradually over about four weeks. And we found that what supports learning is
activity changes in a whole bunch of neurons."
The neurons, the team
discovered, became more selective in responding to an activity associated with
a particular task. The also started reacting faster and more immediately.
"They'll respond really
strongly in advance of one choice and much less so in advance of the other
choice," Churchland said.
When the animals are just
beginning to learn, the neurons don't respond until around the time it makes
the choice. But as the animal gains expertise, the neurons respond much further
in advance.
"We can kind of read the
animal's mind in a way, we can predict what the animal is going to do before he
does it," Churchland said. "When you're a novice at something your
brain is doing all different things, so you have neurons engaged in all
different things. But then when you're an expert, you hone in on exactly what
you're going to do and we can pick up that activity."
The researchers decoded neural
activity by training a small artificial network called the 'Linear Support
Vector Machine' using machine learning algorithms. It collects performance data
from multiple trials and combines it with the activity of all the neurons,
weighing them to make a guess about what the animal's going to do. As the
animal gets better at the task, its neural networks get more refined, precise
and specific. The researchers are able to mirror that onto the artificial
network, which can then predict the animal's decision with about 90 percent
accuracy.
The learning models also offer
another way of looking at specific types of neurons in the brain involved in
cognition, like excitatory and inhibitory neurons, which trigger positive and
negative changes, respectively. In this study, published in Neuron (Cell
Press), the team found that the inhibitory neurons are part of very selective
sub-networks in the brain, and they're strongly selective for the choice that
the animal's going to make.
These neurons are part of a
biophysical model that helps researchers understand how decision-making works.
As researchers refine these models, they're able to make more sense of how
cognition informs behavior.
"We've learned a lot
about perceptual decision-making, the decisions that a subject would get right
and wrong, how long it takes to make those decisions, what the neural activity
would look like during decision-making-by making different kinds of models that
make really concrete predictions," Churchland said. "Now we can
understand, hopefully better, why these very selective sub-networks are there,
how they help us make better decisions, and how they are wired up during
learning."
Story Source:
Materials provided by Cold Spring Harbor Laboratory.
Original written by Charlotte Hu. Note: Content may be edited for style
and length.
Related Multimedia:
Journal Reference:
Farzaneh Najafi, Gamaleldin F.
Elsayed, Robin Cao, Eftychios Pnevmatikakis, Peter E. Latham, John P.
Cunningham, Anne K. Churchland. Excitatory and Inhibitory Subnetworks Are
Equally Selective during Decision-Making and Emerge Simultaneously during
Learning. Neuron, 2019; DOI: 10.1016/j.neuron.2019.09.045
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