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The Transmitter’s favorite features of 2024
Our chosen stories include tales about research misconduct in the lab, a neuroscientist working at the end of the world, and the passing of neuroanatomy’s “great-grandfather,” Harvey Karten.
The Transmitter’s favorite features of 2024
Our chosen stories include tales about research misconduct in the lab, a neuroscientist working at the end of the world, and the passing of neuroanatomy’s “great-grandfather,” Harvey Karten.
Going deep: The Transmitter’s top long-form stories in 2023
Our favorite features and book excerpts from the past year delved into the neurobiology of cancer; problems with survey data; free will; mathematical minds; and questions around one startup’s quest to treat brain conditions with cell therapies.
Going deep: The Transmitter’s top long-form stories in 2023
Our favorite features and book excerpts from the past year delved into the neurobiology of cancer; problems with survey data; free will; mathematical minds; and questions around one startup’s quest to treat brain conditions with cell therapies.
Explore more from The Transmitter
Why neural foundation models work, and what they might—and might not—teach us about the brain
These models can partly generalize across species, brain regions and tasks, suggesting that a set of machine-learnable rules govern neural population activity. But will we be able to understand them?
Why neural foundation models work, and what they might—and might not—teach us about the brain
These models can partly generalize across species, brain regions and tasks, suggesting that a set of machine-learnable rules govern neural population activity. But will we be able to understand them?
Error equation predicts brain’s ability to generalize
Four statistical measurements of neural network geometry capture how well brains and artificial networks use what they already know to solve new problems, a study suggests.
Error equation predicts brain’s ability to generalize
Four statistical measurements of neural network geometry capture how well brains and artificial networks use what they already know to solve new problems, a study suggests.
Embrace complexity to improve the translatability of basic neuroscience
Researchers must learn to view heterogeneity as an essential feature of the systems they study and a central consideration in experimental design, not a variable to control for or reduce.
Embrace complexity to improve the translatability of basic neuroscience
Researchers must learn to view heterogeneity as an essential feature of the systems they study and a central consideration in experimental design, not a variable to control for or reduce.