David Amaral is distinguished professor of psychiatry and behavioral sciences at the University of California, Davis MIND Institute.
David Amaral
Director
Autism BrainNet
From this contributor
‘Prototypical autism’ research is likely a dead end
Efforts to define “frank” or “classic” forms of the condition build on several assumptions that the science has not yet borne out.
‘Prototypical autism’ research is likely a dead end
By
Deborah Fein, David Amaral, Einat Waizbard-Bartov
25 January 2024 | 8 min read
Questions for Amaral, Halladay: Boosting brainpower
A new network of brain banks aims to collect and disburse tissue donations to U.S. autism researchers.
Questions for Amaral, Halladay: Boosting brainpower
By
Katie Moisse
15 December 2015 | 6 min read
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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.
By
Lauren Schenkman
20 May 2025 | 6 min read
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.
By
Jill Adams
20 May 2025 | 2 min read
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.
By
Alona Fyshe
19 May 2025 | 7 min read