Brady Huggett was features editor at The Transmitter, where he wrote and edited features and long-form projects, from 2022 to 2026. He is also the creator and host of the “Synaptic” podcast. Before joining The Transmitter he served as business editor at Nature Biotechnology, and prior to that was the managing editor of BioWorld.
Brady Huggett
Former features editor
The Transmitter
From this contributor
Sylvia Fogel, reshaping U.S. autism research strategy
When autistic kids grow up, Chapter 5: The war dial
When autistic kids grow up, Chapter 4: How did things unfold?
When autistic kids grow up, Chapter 3: Would there be data?
When autistic kids grow up, Chapter 2: “You need to go to college”
Education
- M.A. in creative writing, The New School
- M.A. in journalism, University of North Carolina at Chapel Hill
- B.S. in biology from Wake Forest University in Winston-Salem, North Carolina
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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.