Fred Volkmar is professor of child psychiatry, pediatrics and psychology at the Yale Child Study Center.

Fred Volkmar
Professor
Yale Child Study Center
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Narrowing of ‘autism’ in DSM-5 runs counter to idea of broad spectrum
The strict definition of autism in the latest version of the diagnostic manual is antithetical to the idea that autism comes in a wide variety of forms.

Narrowing of ‘autism’ in DSM-5 runs counter to idea of broad spectrum
Fred Volkmar: A decades-long perspective on autism research
Over the past 30 years, autism research pioneer Fred Volkmar says he has learned that researchers should be humble when assigning meaning to autism behavior, and seek to translate their findings into useful applications.

Fred Volkmar: A decades-long perspective on autism research
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Sharing Africa’s brain data: Q&A with Amadi Ihunwo
These data are “virtually mandatory” to advance neuroscience, says Ihunwo, a co-investigator of the Brain Research International Data Governance & Exchange (BRIDGE) initiative, which seeks to develop a global framework for sharing, using and protecting neuroscience data.

Sharing Africa’s brain data: Q&A with Amadi Ihunwo
These data are “virtually mandatory” to advance neuroscience, says Ihunwo, a co-investigator of the Brain Research International Data Governance & Exchange (BRIDGE) initiative, which seeks to develop a global framework for sharing, using and protecting neuroscience data.
Cortical structures in infants linked to future language skills; and more
Here is a roundup of autism-related news and research spotted around the web for the week of 19 May.

Cortical structures in infants linked to future language skills; and more
Here is a roundup of autism-related news and research spotted around the web for the week of 19 May.
The BabyLM Challenge: In search of more efficient learning algorithms, researchers look to infants
A competition that trains language models on relatively small datasets of words, closer in size to what a child hears up to age 13, seeks solutions to some of the major challenges of today’s large language models.

The BabyLM Challenge: In search of more efficient learning algorithms, researchers look to infants
A competition that trains language models on relatively small datasets of words, closer in size to what a child hears up to age 13, seeks solutions to some of the major challenges of today’s large language models.