We are the NeuroAI research group at the EPFL Neuro-X Institute, jointly between the School of Life Sciences, and the School of Computer and Communication Sciences.
Our research focuses on a computational understanding of the neural mechanisms underlying human natural intelligence. To achieve this goal, we bridge Deep Learning, Neuroscience, and Cognitive Science, building artificial neural network models that match the brain’s neural representations in their internal processing and are aligned to human behavior in their outputs.
We primarily engage on three synergistic research directions:
Brain- and Behavior-Aligned Models
We create state-of-the-art models using task optimization and primate behavioral and neural measurements. We currently focus most on vision and language.
Integrating multimodal representations
We are building brain-like models that connect sensory processing and downstream readout streams, and bridge from neurons to behavior.
Computationally-guided clinical translation
To improve people’s lives we are developing model-guided closed-loop approaches (e.g. for blindness, dyslexia, aphasia).
Selected Publications
Please see Google Scholar for a full list of publications.
TopoLM: brain-like spatio-functional organization in a topographic language model
Rathi*, Mehrer*, AlKhamissi, Binhuraib, Blauch, Schrimpf.
Oral @ ICLR 2025.
[paper]
The LLM Language Network: A Neuroscientific Approach for Identifying Causally Task-Relevant Units
AlKhamissi, Tuckute, Bosselut**, Schrimpf**.
NA ACL 2025.
[paper]
Scaling Laws for Task-Optimized Models of the Primate Visual Ventral Stream
Gokce & Schrimpf.
arXiv 2024.
[paper]
Driving and Suppressing the Human Language Network Using Large Language Models
Tuckute, Sathe, Srikant, Taliaferro, Wang, Schrimpf, Kay, Fedorenko.
Nature Human Behavior 2024.
[paper] [code] [press 1 2 3 4 5 6 7 8 9]
Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness
Dapello*, Kar*, Schrimpf, Geary, Ferguson, Cox, DiCarlo.
Notable Top-5% @ ICLR 2023.
[paper] [code] [press 1 2 3]
The Neural Architecture of Language: Integrative Modeling Converges on Predictive Processing
Schrimpf, Blank, Tuckute, Kauf, Hosseini, Kanwisher, Tenenbaum, Fedorenko.
PNAS 2021.
[paper] [code, now also in Brain-Score] [press 1 2 3 4 5 6 7 8 9 10 11 12 13]
Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations
Dapello*, Marques*, Schrimpf, Geiger, Cox, DiCarlo.
Spotlight @ NeurIPS 2020.
[paper] [code] [press 1 2]
CORnet: Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs
Kubilius*, Schrimpf*, Hong, Majaj, Rajalingham, Issa, Kar, Bashivan, Prescott-Roy, Schmidt, Nayebi, Bear, Yamins, DiCarlo.
Oral @ NeurIPS 2019.
[paper] [code] [video] [interview] [press 1 2 3]
Brain-Score platform
ongoing community effort; first released in 2018.
[website] [perspective Neuron 2020] [technical paper 2018] [code] [press 1 2 3]
Team
Former members:
-
Robert Adam Pieniuta (Master Thesis intern, 2024)
-
Aleksei Kudrinskii (Master Thesis intern, 2024)
- Neil Rathi (Summer@EPFL intern 2024)
- Ayati Sharma (Summer@EPFL intern 2024)
- Narek Alvandian (Master Thesis intern 2023-2024)
- Maisa Ben Salah (Master Thesis intern, 2023-2024 –> intern at Stanford)
- Ernesto Bocini (Master Thesis intern 2023-2024 –> Logitech)
- Khai Loong Aw (Summer@EPFL intern 2023 –> PhD program at Stanford)
Prospective Members
- BS/MS interns and project students: If you are an EPFL student or if you are considering a longer-term visit (6-12 months full-time), please submit your application via this form. If you are not an EPFL student, please consider the SRP and Summer@EPFL programs — please do not email us. We generally only support ThinkSwiss applications for previous interns/collaborators. Please check the (non-comprehensive) list of projects.
- PhD Applicants: We are always looking for highly talented and motivated students. At EPFL you apply to central PhD programs rather than an individual lab directly. Specifically, we hire from the EDIC and EDNE programs. To keep things fair for all applicants, we typically do not hold meetings before the initial screening. You do not need to send an email to us.
- Postdocs: Please email Martin Schrimpf directly with your CV, cc’ing Stéphanie Debayle. Consider applying to postdoctoral fellowships from SNSF, NeuroX, the EPFL AI Center (and EPFLeader4impact when it reopens), and an SNSF Starting Grant. We can currently only consider postdocs who come with their own funding.
Teaching
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BIOENG-310 Neuroscience foundations for engineers (Bachelor)
This overview course bridges computational expertise with neuroscience fundamentals, aimed at fostering interdisciplinary communication and collaboration for engineering-based neuroscience programs. - NX-414 Brain-like computation and intelligence (Master)
Recent advances in machine learning have contributed to the emergence of powerful models of animal perception and behavior. In this course we will compare the behavior and underlying mechanisms in these models as well as brains.
News
Contact
Offices:
BC 206 (Main Campus Lausanne)
B1 0 261.050 (Campus Biotech Geneva)
Administrative Assistant: Stéphanie Debayle
Office: SV 2513
Phone: +41 21 693 5148
Email: stephanie.debayle@epfl.ch
Mailing address:
EPFL INX-SV SV UPSCHRIMPF1, SV 2513 (Bâtiment SV), Station 19, CH-1015 Lausanne
Access map
Funding
Many thanks to the following funding bodies for supporting our research:
