Nora Bradford is a lecturer in the Critical Writing Program at the University of Pennsylvania. She completed a Ph.D. in cognitive science at the University of California, Irvine and a B.S. in neuroscience, psychology and philosophy at the University of Chicago. Bradford has previously written freelance articles for Spectrum.
Nora Bradford
Lecturer, Critical Writing Program
University of Pennsylvania
From this contributor
‘Friction-maxxing’ in school: Students should read primary literature, not AI summaries
Trainees need to learn how to identify a neuroscience paper’s major takeaways and integrate them into their understanding. This skill doesn’t come from outsourcing the work to large language models.
‘Friction-maxxing’ in school: Students should read primary literature, not AI summaries
Epigenome edits unmute MECP2 in Rett-like neurons
The approach removes methyl tags from the gene and shields it from other silencing factors without changing the gene itself, raising hopes for a new treatment.
Epigenome edits unmute MECP2 in Rett-like neurons
Common and rare autism-linked variants share functional effects
Within the 16p region of the genome, the two types of variants similarly decrease neuronal gene expression — an effect that may reflect their spatial relationship.
Common and rare autism-linked variants share functional effects
Auditory cortex may develop early in autism
A well-studied brain response to sound appears earlier than usual in young children with autism.
Auditory cortex may develop early in autism
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.