A plasticity rule that predicts which memories survive sleep
A single local learning rule reproduces overnight consolidation across three published datasets.
Computational Neuroscience
I build mathematical models of learning and memory and test them against large-scale neural recordings — where theory, data, and machine learning meet.
Assistant Professor · Department of Neuroscience · Brandt Institute of Technology
About
I'm a computational neuroscientist. My lab asks how the brain turns experience into lasting change — how a spiking network reshapes itself to hold a memory, and why some memories stabilize while others fade.
We work in both directions: building theoretical models of synaptic plasticity, then testing them against large-scale recordings from collaborators' labs. Increasingly that means borrowing tools from machine learning — and asking, in return, what the brain can teach us about learning algorithms. The goal is models that don't just fit the data, but explain it.
Selected Publications
A representative selection. Full list and preprints on the CV and lab site.
A single local learning rule reproduces overnight consolidation across three published datasets.
How a population can keep a memory legible even as individual tuning slowly drifts.
Architectural priors from cortex improve sample efficiency on sequence tasks.
Low-dimensional structure in neural activity generalizes across two behaviors.
The Raman Lab
Everything the lab builds ships as documented, reusable code. Our plasticity-rule library has been downloaded by groups on four continents, and we maintain a public benchmark for comparing biological learning rules against machine-learning baselines. We train students to write code other people can actually run.
Teaching
Talks & Service
Get in touch
Prospective PhD students: please include your research interests and CV. Journalists: note your deadline in the subject line.