EPFL NeuroAI Lab ‐ EPFL

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.

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]

Primate Inferotemporal Cortex Neurons Generalize Better to Novel Image Distributions Than Analogous Deep Neural Networks Units
Bagus, Marques, Sanghavi, DiCarlo, Schrimpf.
SVRHM @ NeurIPS 2023.
[paper]

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] [code] [press 1 2 3]