Evan Schaffer is assistant professor of neuroscience at the Friedman Brain Institute at the Icahn School of Medicine at Mount Sinai. His lab uses mathematical tools to understand distributed computations in the brain, identify how these computations change with learning and identify how feedback from the body impacts cognition. Schaffer received his Ph.D. at the Center for Theoretical Neuroscience, in Larry Abbott’s lab at Columbia University. He completed his postdoctoral work in Richard Axel’s Lab at Columbia University
Evan Schaffer
Assistant professor of neuroscience
Icahn School of Medicine at Mount Sinai
Selected articles
- “Inhibitory stabilization of the cortical network underlies visual surround suppression” | Neuron
- “A complex-valued firing-rate model that approximates the dynamics of spiking networks” | PLoS Computational Biology
- “Odor perception on the two sides of the brain: Consistency despite randomness” | Neuron
- “The spatial and temporal structure of neural activity across the fly brain” | Nature Communications
- “Behavioral fingerprinting of the naked mole-rat uncovers signatures of eusociality and social touch” | bioRxiv
Explore more from The Transmitter
The fast-expanding repertoire of mitochondria in the brain
More than cellular powerhouses, these organelles also seem to help synapses communicate, support memory formation and even shape behavior.
The fast-expanding repertoire of mitochondria in the brain
More than cellular powerhouses, these organelles also seem to help synapses communicate, support memory formation and even shape behavior.
By
Giorgia Guglielmi
3 July 2026 | 7 min read
When autistic kids grow up, Chapter 5: The war dial
“You have to reshape the whole system.” Tempest McDonald earns a measure of peace.
When autistic kids grow up, Chapter 5: The war dial
“You have to reshape the whole system.” Tempest McDonald earns a measure of peace.
By
Brady Huggett
2 July 2026 | 42 min listen
Scientists decry conference’s use of hidden prompts to snare AI peer reviews
The invisible messages, which instruct large language models to use telltale phrases in a peer-review report, are effective in catching artificial-intelligence misuse but also erode trust, some say.
Scientists decry conference’s use of hidden prompts to snare AI peer reviews
The invisible messages, which instruct large language models to use telltale phrases in a peer-review report, are effective in catching artificial-intelligence misuse but also erode trust, some say.
By
Dalmeet Singh Chawla
1 July 2026 | 4 min read