A groundbreaking UC San Diego study has uncovered how different brain synapses follow unique learning rules, challenging long-held assumptions. The research used cutting-edge imaging to track synaptic changes in mice, revealing implications for AI and neurological disorders. Scientists found that neurons don’t operate under a single plasticity rule, opening doors for advanced treatments. These insights could revolutionize how we approach conditions like autism, PTSD, and Alzheimer's.
April 24, 2025
Groundbreaking study reveals how our brain learns
"Our
research provides a clearer understanding of how synapses are being modified
during learning, with potentially important health implications." –
William "Jake" Wright
Neurobiologists
using cutting-edge visualisation techniques have revealed how changes across
our synapses and neurons unfold.
Key
Points
1 Study
reveals neurons follow multiple learning rules, not uniform plasticity
2 Findings
could reshape AI neural network designs
3 Offers
insights for treating Alzheimer's and autism
4 Uses
advanced two-photon imaging to track synaptic changes
The
findings depict how information is processed in our brain's circuitry, offering
insights for neurological disorders and brain-like AI systems.
How do we
learn something new? How do tasks at a new job, lyrics to the latest hit song
or directions to a friend's house become encoded in our brains?
The broad
answer is that our brains undergo adaptations to accommodate new information.
To follow a new behaviour or retain newly introduced information, the brain's
circuitry changes.
Such
modifications are orchestrated across trillions of synapses -- the connections
between individual nerve cells, called neurons -- where brain communication
takes place.
In an
intricately coordinated process, new information causes certain synapses to get
stronger with new data while others grow weaker. Neuroscientists who have
closely studied these alterations, known as "synaptic plasticity,"
have identified numerous molecular processes causing such plasticity.
Yet an
understanding of the "rules" selecting which synapses undergo this
process remained unknown, a mystery that ultimately dictates how learned information
is captured in the brain.
University
of California, San Diego neurobiologists William "Jake" Wright,
Nathan Hedrick and Takaki Komiyama have now uncovered key details about this
process.
The main
financial support for this multi-year study was provided by several National
Institutes of Health research grants and a training grant.
As
published April 17 in the journal Science, the researchers used a cutting-edge
brain visualisation methodology, including two-photon imaging, to zoom into the
brain activity of mice and track the activities of synapses and neuron cells
during learning activities.
With the
ability to see individual synapses like never before, the new images revealed
that neurons don't follow one set of rules during episodes of learning, as had
been assumed under conventional thinking.
Rather,
the data revealed that individual neurons follow multiple rules, with synapses
in different regions following different rules. These new findings stand to aid
advancements in many areas, from brain and behaviour disorders to artificial
intelligence.
"When
people talk about synaptic plasticity, it's typically regarded as uniform
within the brain," said Wright, a postdoctoral scholar in the School of
Biological Sciences and first author of the study.
"Our
research provides a clearer understanding of how synapses are being modified during
learning, with potentially important health implications since many diseases in
the brain involve some form of synaptic dysfunction."
Neuroscientists
have carefully studied how synapses only have access to their own
"local" information, yet collectively they help shape broad new
learned behaviours, a conundrum labelled as the "credit assignment
problem."
The issue
is analogous to individual ants that work on specific tasks without knowledge
of the goals of the entire colony.
The new
information offers promising insights for the future of artificial intelligence
and the brain-like neural networks upon which they operate.
Typically,
an entire neural network functions on a common set of plasticity rules, but
this research infers possible new ways to design advanced AI systems using
multiple rules across singular units.
For
health and behaviour, the findings could offer a new way to treat conditions
including addiction, post-traumatic stress disorder and Alzheimer's disease, as
well as neurodevelopmental disorders such as autism.
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