Tiffany Woynaroski is assistant professor of hearing and speech sciences at Vanderbilt University Medical Center, Vanderbilt Kennedy Center, Vanderbilt Brain Institute, and Frist Center for Autism and Innovation in Nashville, Tennessee.
Tiffany Woynaroski
Assistant professor
Vanderbilt University
From this contributor
Common sensory response scores may miss important variations
A person’s “overall” score on sensory-seeking, hyperreactive or hyporeactive tendencies may obscure nuances in their individual sensory experience.
Common sensory response scores may miss important variations
Errors of omission: Why we are deeply concerned about research on autism therapies
Studies of autism treatments rarely report adverse events, and the scientists involved often fail to disclose their conflicts of interest.
Errors of omission: Why we are deeply concerned about research on autism therapies
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The term ‘model organism’ should be reserved for biological systems
By referring to artificial neural networks as model organisms, we run the risk of distorting funding priorities and disregard the explanatory power of a shared evolutionary history across biological organisms.
The term ‘model organism’ should be reserved for biological systems
By referring to artificial neural networks as model organisms, we run the risk of distorting funding priorities and disregard the explanatory power of a shared evolutionary history across biological organisms.
New phase of BRAIN Initiative spawns basal ganglia ‘parts list’
With a standardized cell naming system and infrastructure for data collection and analysis, the basal ganglia project sets the stage for a whole-brain primate atlas.
New phase of BRAIN Initiative spawns basal ganglia ‘parts list’
With a standardized cell naming system and infrastructure for data collection and analysis, the basal ganglia project sets the stage for a whole-brain primate atlas.
Machine-learning model spots autism-linked variants that scramble splicing
The model provides a way to investigate genetic variation in regions of the genome whose functions have been difficult to interpret.
Machine-learning model spots autism-linked variants that scramble splicing
The model provides a way to investigate genetic variation in regions of the genome whose functions have been difficult to interpret.