Washington, D.C., April 21 (ANI) — Neurobiologists using cutting‑edge visualization techniques have uncovered how changes across our synapses and neurons unfold as we learn, offering fresh insights for treating neurological disorders and designing brain‑inspired AI systems.
How do we learn something new—whether it’s a task at work, the lyrics to a hit song or directions to a friend’s house? Broadly, our brains undergo adaptations to encode new information. Learning relies on coordinated modifications across trillions of synapses—the connections between individual neurons—where communication in the brain takes place.
In a process known as synaptic plasticity, certain synapses strengthen in response to new data, while others weaken. Although researchers have identified many molecular pathways underlying plasticity, they lacked an understanding of the “rules” determining which synapses change—a mystery fundamental to how learning is stored in neural circuits.
Researchers at the University of California, San Diego—William “Jake” Wright, Nathan Hedrick and Takaki Komiyama—have now filled that gap. Their multi‑year study, supported by National Institutes of Health grants, was published April 17 in the journal Science.
Using two‑photon imaging to observe individual synapses in live mice during learning tasks, the team discovered that neurons do not follow a single, uniform rule when altering synaptic strength. Instead, individual neurons apply multiple plasticity rules, with different synaptic regions responding according to distinct mechanisms.
“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 modified during learning, with potentially important health implications since many brain diseases involve synaptic dysfunction.”
This finding also addresses the “credit assignment problem”—the question of how individual synapses, which only access local information, collectively orchestrate complex learned behaviors. The challenge is analogous to how ants, each following local cues, contribute to the colony’s overall goals.
Beyond basic neuroscience, the team’s work suggests new approaches for artificial intelligence. Current neural networks typically use a single plasticity rule across all units; adopting multiple, localized rules could yield more powerful, adaptive AI models.
Clinically, these insights could inform treatments for conditions linked to synaptic dysregulation, including addiction, post‑traumatic stress disorder, Alzheimer’s disease and neurodevelopmental disorders such as autism.
— ANI
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