Felicia Davatolhagh is a postdoctoral researcher at the University of California, Los Angeles in Anne Churchland’s lab, where she studies how cortical circuits are altered during decision-making in a genetic mouse model of autism. She also serves as a member of the neurobiology department’s Justice, Diversity and Inclusion (JEDI) group.
Felicia Davatolhagh
Postdoctoral researcher
University of California, Los Angeles
From this contributor
Women are systematically under-cited in neuroscience. New tools can change that.
An omitted citation in a high-profile paper led us to examine our own practices and to help others adopt tools that promote citation diversity.
Women are systematically under-cited in neuroscience. New tools can change that.
By
Anne Churchland, Felicia Davatolhagh
22 July 2024 | 5 min listen
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.
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