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.
Explore more from The Transmitter
What a bird’s-eye view of half a million papers reveals about neuroscience
New research uses artificial-intellligence-driven bibliometrics to map the structural organization of neuroscience across 25 years. The field it reveals is at once thriving and theoretically adrift.
What a bird’s-eye view of half a million papers reveals about neuroscience
New research uses artificial-intellligence-driven bibliometrics to map the structural organization of neuroscience across 25 years. The field it reveals is at once thriving and theoretically adrift.
Newly identified barrier cells seal off choroid plexus from CSF, rest of brain
A long-overlooked layer of fibroblasts exists inside the choroid plexus of mice and humans, adding complexity to the area’s compartmentalization.
Newly identified barrier cells seal off choroid plexus from CSF, rest of brain
A long-overlooked layer of fibroblasts exists inside the choroid plexus of mice and humans, adding complexity to the area’s compartmentalization.
‘Digital sphinx’ raises questions about connectome models
The sphinx, with a worm’s brain and a fly’s body, illustrates the potential pitfalls of using deep-learning techniques to model biological processes.
‘Digital sphinx’ raises questions about connectome models
The sphinx, with a worm’s brain and a fly’s body, illustrates the potential pitfalls of using deep-learning techniques to model biological processes.