Michael Ehlers
Neuroscience Chief Scientific Officer
Pfizer
From this contributor
A cautionary tale for autism drug development
Poorly designed animal drug studies for motor disorders have led to spurious conclusions for the clinical trials that follow. This may be even more true for autism research, says Michael Ehlers.
SHANK mutations converge at neuronal junctions in autism
SHANK3, one of the strongest candidate genes for autism, has the potential to be a molecular entry point into understanding the synaptic, developmental and circuit origins of the disorder.
SHANK mutations converge at neuronal junctions in autism
Drug zone
Rodent and stem cell models remain challenging for developing psychiatric drugs, says Michael Ehlers, chief scientific officer of neuroscience at Pfizer.
Explore more from The Transmitter
Autism-linked genes alter sleep behavior, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 13 April.
Autism-linked genes alter sleep behavior, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 13 April.
This paper changed my life: Erin Calipari ponders the nuances of rewarding and aversive stimuli
A 1960s study by Kelleher and Morse found that lever pressing in squirrel monkeys depended not on whether they received a reward or shock, but on the rules of the task. This taught Calipari to think deeply about factors that influence how behavior is generated and maintained.
This paper changed my life: Erin Calipari ponders the nuances of rewarding and aversive stimuli
A 1960s study by Kelleher and Morse found that lever pressing in squirrel monkeys depended not on whether they received a reward or shock, but on the rules of the task. This taught Calipari to think deeply about factors that influence how behavior is generated and maintained.
Why neural foundation models work, and what they might—and might not—teach us about the brain
These models can partly generalize across species, brain regions and tasks, suggesting that a set of machine-learnable rules govern neural population activity. But will we be able to understand them?
Why neural foundation models work, and what they might—and might not—teach us about the brain
These models can partly generalize across species, brain regions and tasks, suggesting that a set of machine-learnable rules govern neural population activity. But will we be able to understand them?