Richard Bethlehem is a postdoctoral fellow and research associate at the Autism Research Centre and Brain Mapping Unit at the University of Cambridge in the United Kingdom. He studies integrated neuroimaging and transcriptomics to gain better understanding of the biological underpinnings of typical and atypical neurodevelopment.
Richard Bethlehem
Research associate
University of Cambridge
From this contributor
Q&A with Richard Bethlehem: What goes into a Brainhack
Brainhack conferences offer talks and hands-on tutorials, and unite small groups of interdisciplinary researchers to work on open-source neuroscience projects.
Q&A with Richard Bethlehem: What goes into a Brainhack
By
Niko McCarty
9 July 2021 | 7 min read
How normative modeling can reframe autism’s heterogeneity
Normative modeling could capture variability among autistic people and allow for individualized assessments.
How normative modeling can reframe autism’s heterogeneity
By
Richard Bethlehem, Andre F. Marquand
25 February 2020 | 4 min read
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Jill Adams
14 April 2026 | 2 min read
This paper changed my life: Erin Calipari ponders the nuances of rewarding and aversive stimuli
A 1960s study by Kelleher and Morse found that lever pressing in squirrel monkeys depended not on whether they received a reward or shock, but on the rules of the task. This taught Calipari to think deeply about factors that influence how behavior is generated and maintained.
This paper changed my life: Erin Calipari ponders the nuances of rewarding and aversive stimuli
A 1960s study by Kelleher and Morse found that lever pressing in squirrel monkeys depended not on whether they received a reward or shock, but on the rules of the task. This taught Calipari to think deeply about factors that influence how behavior is generated and maintained.
By
Erin Calipari
14 April 2026 | 5 min read
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?
By
Juan Gallego
13 April 2026 | 8 min read