Scientists have often wondered how the brain can take in new experiences without erasing stored memories. However, a new study in Nature now identifies a circuit-level mechanism that may help answer this question—and it may also offer clues for improving artificial intelligence.
Researchers at NYU Langone Health recorded the activity of hundreds of neurons simultaneously across several brain regions in mice. By tracking how signals move from the hippocampus to the neocortex, where long-term memories are kept, they found that a subset of hippocampal neurons appears to participate in routing information between regions in distinct patterns. These cells participate in both incoming and outgoing signaling, but do so using distinct patterns of activity.
Separate Channels Through the Same Cells
The study focused on a series of interconnected brain regions: CA3, which processes rapidly changing information; CA1, which serves as a central hub; and the retrosplenial cortex, which is important for navigation and spatial memory. About one in four CA1 neurons receives most of the signals from CA3. When these same neurons send signals to the retrosplenial cortex, they fire in a different pattern, creating a separate outgoing channel using the same cells.
Rather than using new neurons, the brain separates these channels by changing how the same neurons fire together. The researchers suggest this arrangement may help reduce interference between new learning and more stable cortical memories.
“Our findings help explain how memory can be both moldable and enduring,” said co-lead author Joaquín Gonzalez, PhD, a postdoctoral fellow in the Department of Psychiatry at NYU Grossman School of Medicine. “By changing how the same cells fire together instead of turning on new cells, the brain can keep information organized and protect older memories.”
What Happens During Sleep
The CA1 neurons involved in learning during the day also stay active at night. The researchers found that these cells remain busy during sleep events called sharp-wave ripples, which are bursts of activity the hippocampus uses to replay and strengthen memories from the day.
Since overlapping neurons are recruited during both waking activity and sleep replay, the same circuit elements appear to participate in both learning and consolidation. This theoretically allows the brain to use a single system for both learning and storing memories, without the two processes interfering with each other.
“Our study shows how learning and memory consolidation can coexist in the same network,” said co-lead author Mihály Vöröslakos, MD, PhD. “Our discovery was made possible because for the first time, we were able to record hundreds of individual neurons across all the key regions simultaneously in animals that were moving around naturally.”
Implications for Disease and AI
The system identified in this research may help explain how memory disorders begin. In Alzheimer’s disease, for example, the hippocampal circuits examined here are some of the earliest to deteriorate. These networks are essential for sorting and organizing new experiences.
“Our discovery of a ‘memory switchboard’ deep in the hippocampus may provide clues as to how memory circuits fail in Alzheimer’s disease,” said co-senior author Zhe S. Chen, PhD, a professor in the Departments of Psychiatry and Neuroscience at NYU Grossman School of Medicine.
Artificial intelligence systems have a hard time holding onto what they’ve already learned when faced with new tasks, a problem researchers call catastrophic forgetting. The human brain appears able to update memories without catastrophic forgetting by altering the use of existing neural circuits. By studying how our brains pull this off, scientists hope to design AI that can keep learning new things without forgetting the old.
“By showing how the mammalian brain can safeguard memories during learning, our research may offer a biological blueprint for designing next-generation AI technology that can update itself continuously without overwriting what it has already acquired,” said co-senior author György Buzsáki, MD, PhD, the Biggs Professor of Neuroscience at NYU Grossman School of Medicine.
The researchers plan to investigate whether similar switchboard-like channels exist in other memory circuits. The authors caution that their findings in mice under controlled conditions do not yet allow conclusions about the human brain.
Austin Burgess is a writer and researcher with a background in sales, marketing, and data analytics. He holds an MBA, a Bachelor of Science in Business Administration, and a data analytics certification. His work focuses on breaking scientific developments, with an emphasis on emerging biology, cognitive neuroscience, and archaeological discoveries.