Foundation models & learning architectures
We develop self-supervised models and neural architectures that learn transferable representations from large-scale brain recordings.
Research directions
Research & projectsNeural dynamics, decoding & control
We build dynamical models that reveal how neural populations encode behavior, respond to inputs, and support real-time decoding.
Neural imaging & multimodal learning
We learn from neural images and complementary signals, capturing brain dynamics across subjects, modalities, and timescales.
BaRISTA
Self-supervised learning from human intracranial recordings, with spatial encoding and masking designed around the brain’s anatomical scales.
DPAD
Recurrent neural networks separate behavior-related brain dynamics, improving predictions and revealing compact neural representations.
WiCAT
Atlas-aligned representations transfer across subjects and tasks in widefield calcium imaging, supporting behavior decoding and reconstruction.
Selected work
Explore publications
Dissociative and prioritized modeling of behaviorally relevant neural dynamics using recurrent neural networks
DPAD uses recurrent neural networks to separate and prioritize behaviorally relevant neural dynamics. It improves neural and behavioral prediction, preserves behavior information in low-dimensional representations, and tests where nonlinearities arise in neural-to-behavior transformations.
Dynamical flexible inference of nonlinear latent factors and structures in neural population activity
DFINE combines nonlinear neural manifolds with tractable latent dynamics to enable real-time filtering, offline smoothing and inference with missing observations, while improving neural and behavioral prediction across diverse datasets.
Modelling and prediction of the dynamic responses of large-scale brain networks during direct electrical stimulation
Dynamic input-output models predict how temporally varying electrical stimulation changes activity across brain regions, providing a basis for precise neuromodulation and model-based control.
Mood variations decoded from multi-site intracranial human brain activity
Personalized models tracked mood changes from brain recordings in seven people with epilepsy, using intermittent self-reports for training. The study established initial evidence that distributed neural activity can reveal mood variations over several days.
Our people
Meet the team
Principal investigator
Maryam M. Shanechi
- Alexander A. Sawchuk Chair in Electrical and Computer Engineering
- Professor of Electrical and Computer Engineering, Biomedical Engineering, and Computer Science
- Founding Director, USC Center for Neurotechnology
We bring together researchers in machine learning, engineering, and neuroscience to understand brain activity and develop neurotechnology.
News
All newsJoin us
Postdoctoral and PhD opportunities in machine learning,
neural engineering, and computational neuroscience.