Anila D’Mello is an assistant professor and Jon Heighten Scholar in Autism Research in the Department of Psychiatry and O’Donnell Brain Institute at the University of Texas Southwestern in Dallas.
Anila D’Mello
Assistant professor
University of Texas Southwestern
From this contributor
How scientists can counteract their unwitting contributions to autism’s sex bias
Common diagnostic and research practices may be adding to autism’s sex bias, but there are some simple steps scientists can take to counteract it.
How scientists can counteract their unwitting contributions to autism’s sex bias
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Autism-linked genes alter sleep behavior, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 13 April.
Autism-linked genes alter sleep behavior, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 13 April.
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.
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?