Erfan Zabeh

I am a Postdoctoral Research Scientist at the Center for Theoretical Neuroscience, Zuckerman Mind Brain Behavior Institute at Columbia University, working with Larry F. Abbott.

With tens of billions of parameters, today's foundational models approach the scale of biological neural circuits—and the theoretical study of artificial and natural intelligence is converging. I use theory as a bridge between the two, interchangeably drawing insights from neuroscience and machine learning to uncover the principles that govern intelligent behavior in both biological brains and AI systems.

I earned my Ph.D. in Biomedical Engineering from Columbia University in the Zuckerman Institute, where I was advised by Joshua Jacobs and Jacqueline Gottlieb, and started as a RISE Prize Fellow. Before that, I studied Electrical Engineering and Physics at Sharif University of Technology, and was a medalist of the International Olympiad on Astronomy and Astrophysics.

My publications are listed below, or see Google Scholar. My CV is here.

Research Program

LLMs in Dry Labs: The dream of neuroscience is a clear picture of the brain during behavior—cell-type-specific recordings, synaptic-level connectomes, whole-brain imaging. We're getting closer, but the brain remains stubbornly opaque. In parallel, artificial neural networks are racing ahead: they already exhibit complex, human-like behaviors while granting us full access to every weight, activation, and gradient—tangible parameters producing intangible complexity. For the first time, we can study intelligence the way physicists study controlled systems—with complete observability and unlimited intervention. LLMs are the new model organisms of cognition, and computational theory is the microscope. I'm building a research program around this vision—if it resonates with you, let's talk or explore some ongoing projects.

Service for Wet Labs: To ground these theoretical insights in biological reality, I've built and sustained close collaborations with leading experimental labs throughout my PhD, translating cutting-edge optical and electrophysiological recordings into testable model predictions, and iteratively refining both models and experiments in tandem. Leveraging recent advances in AI and machine learning, I continue developing analysis tools, scaling from single dendrites to brain-wide activity. By sustaining this bidirectional workflow, I aim to advance our understanding of neural computation and help bridge the divide between theoretical and experimental neuroscience for the next generation.

Education

Teaching & Mentorship

I genuinely love working with students! Whether you're an undergrad curious about research, a master's student looking for a thesis project, or a PhD student wanting to collaborate—I'd be happy to chat. I believe the best mentorship happens when we learn from each other, so don't hesitate to reach out even if you're just exploring ideas. If any of my research areas excite you, or if you have a cool project in mind, drop me an email—I'm always excited to meet new people and see where the conversation goes!

Current & Recent Students:

Recent Teaching Experiences:

Publications

For a complete list, see Google Scholar.

2026
K. Fallah, H.N. Chen, R. Singha, E. Kong, G. Turi, A. Losonczy, E. Zabeh*. NeuralFieldManifold: Reconstruction of LFP manifold with Lag Embedding. Submitted to ICML.
E. Zabeh*, Y. Xin, E. Kong, A. Losonczy. DestinODE: Predicting Representational Drift from Connectome Structure with a Physics-Informed Neural Network. Submitted to ICML.
2025
K. Jalaldoust, E. Zabeh*. A Causal Formulation of Spike-Wave Duality. NeurIPS 2025 Workshop on CauScien: Uncovering Causality in Science, 2025.
A. Das, J. Zhang, E. Zabeh, B. Ermentrout, J. Jacobs. Hidden Spirals Reveal Neurocomputational Mechanisms of Traveling Waves in Human Memory. bioRxiv, 2025.
T. Gedankien, J. Kriegel, E. Zabeh, D. McDonagh, B. Lega, J. Jacobs. Cholinergic blockade reveals role for human hippocampal theta in encoding but not retrieval. bioRxiv, 2025.
Jung, T., Zeng, N., Fabbri, J.D., Eichler, G., Li, Z., Zabeh, E., Das, A., Willeke, K., Wingel et al. Stable, chronic in-vivo recordings from a fully wireless subdural-contained 65,536-electrode brain-computer interface device. Revise and Resubmit at Nature Electronics, 2025.
E. Kong*, E. Zabeh*, Z. Liao*, A. Losonczy, T. Geiller. Recurrent connectivity captures distinct spatial memory features along the proximodistal axis of hippocampal CA3. Revision and Resubmit at Neuron.
2024
A. Das, E. Zabeh, G.B. Ermentrout, J. Jacobs. Diverse Spatial Patterns of Traveling Waves Distinguish Cognitive States of Human Memory Representations. Submitted to Cell Journal, 2024.
2023
E. Zabeh, N.C. Foley, J. Jacobs, J.P. Gottlieb. Beta traveling waves in monkey frontal and parietal areas encode recent reward history. Nature Communications, 2023. [pdf] [code] [data]
A. Das, E. Zabeh, J. Jacobs. Detection and analysis of traveling waves in human intracranial EEG oscillations. Handbook of Intracranial EEG for Cognitive Neuroscience, 2023. [chapter]
2022
E. Zabeh, M. Mahdavi, H. Choubdar, R. Lashgari, H. Omrani. Neural and clinical investigation of pregabalin effectivity in treatment of neurological movement disorder. Frontiers in Human Neuroscience, 2022. [pdf] [preprint]
H. Choubdar, M. Mahdavi, Z. Rostami, E. Zabeh, M. Gillies, A. Green, T. Aziz, R. Lashgari. Neural Oscillatory Characteristics of Feedback Associated Activity in Globus Pallidus Interna. eNeuro, 2022. [pdf] [preprint]
A. Vafaei, M. Mohammadi, A. Khadir, E. Zabeh, F. Yazdani, M. Khorasani, R. Lashgari. V1 receptive field structure contributes to neuronal response latency. bioRxiv, 2022.
Z. Fazlali, Y. Ranjbar, E. Zabeh, E. Arabzadeh. Stimulation of Locus Coeruleus Noradrenergic System Modulates Sensory Processing and Brain State in two different time scales. eLife (under review), 2022.
2021
B. Maas*, E. Zabeh*, S. Arabshahi*. QuickTumorNet: Fast Automatic Multi-Class Segmentation of Brain Tumors. IEEE/EMBS Neural Engineering 2021. [pdf] [code]
M. Mahdavi*, H. Choubdar*, E. Zabeh*, M. Rieder, S. Safavi-Naeini, V. Khanlarzadeh, Z. Jobbagy, A. Ghorbani, A. Abedini, A. Kiani, R. Lashgari. A machine learning based exploration of COVID-19 mortality risk. PLOS ONE, 2021. [pdf] [code] [data]
2020
J. Gottlieb, M. Cohanpour, Y. Li, N. Singletary, E. Zabeh. Curiosity, information demand and attentional priority. Current Opinion in Behavioral Sciences, 2020. [pdf]
2019
E. Zabeh, M. Kheirkhah, P. Delavari, A. Ghazizadeh. EEG Alpha waves are induced by activities in superficial depths of the cerebral cortex. bioRxiv, 2019. [preprint]
2017
E. Zabeh, J. Jin, R. Lashgari, J. M. Alonso. 100 thalamic afferents per cortical point are sufficient to accurately map on and off retinotopy in cat visual cortex. Society for Neuroscience, 2017 (poster).

Awards & Honors

Leadership & Service