Jacqueline Crawley is professor emeritus of psychiatry and behavioral sciences at the University of California, Davis.
Jacqueline Crawley
Professor
University of California, Davis
From this contributor
Optimizing behavioral assays for mouse models of autism
As the number of autism rodent models climbs, it is a good time for the field to step back and consider the best practices for assessing autism-like symptoms in rodents, says Jacqueline Crawley.

Optimizing behavioral assays for mouse models of autism
Transparent reports
New standards for animal studies, including an emphasis on replicating results and the publication of negative findings, are vital for research progress, says Jacqueline Crawley.
Promises and limitations of mouse models of autism
Good mouse models of autism, and accurate tests to assay their phenotypes, are key to both narrowing down a cause and developing effective treatments, argues expert Jacqueline Crawley.

Promises and limitations of mouse models of autism
Explore more from The Transmitter
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.