Thomas Serre is a French neuroscientist and computer scientist and the Thomas J. Watson, Sr. Professor of Science and Professor of Cognitive & Psychological Sciences and Computer Science at Brown University • He serves as Faculty Director of the Center for Computation & Visualization and Associate Director of the Center for Computational Brain Science, and is also an affiliate of the Carney Institute for Brain Science, the Center for Theoretical Physics and Innovation, and the Data Science Institute. He holds an International Chair in AI at the Artificial and Natural Intelligence Toulouse Institute (France) and is a Fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS) • Dr. Serre received his Ph.D. in Neuroscience from MIT (2006) and an M.Sc. in Electrical Engineering and Computer Science from Télécom Bretagne, France (2000) • His research investigates the computational principles of biological vision to understand how the brain works—and to build AI systems that see and reason more like humans. He studies how recurrent and feedback processes support visual reasoning, develops cognitive-psychology-inspired benchmarks to reveal gaps between human and machine vision, and designs brain-inspired computational models that help bridge neuroscience and artificial intelligence • His work has been featured by the BBC, The Economist, New Scientist, and Scientific American • Dr. Serre serves as an area chair for premier conferences such as CVPR, ICML, ICLR, and NeurIPS, and as a Senior Editor of Minds, Machines, and Brains (MIT Press) • He has received an NSF Early Career Award, a DARPA Young Faculty Award, a DARPA Director's Award, and Brown's Mid-Career Research Achievement Award (2024), and his team's work on human action recognition earned the IEEE PAMI Helmholtz Prize (2021) and the Mark Everingham Prize (2022).

Research

A model can recognize thousands of object categories yet struggle to trace a contour, judge a relation, or imagine a scene from another viewpoint. We study these discrepancies to identify the computations that support biological vision.

We combine behavioral and neural experiments with computational models of recurrent visual processing and learning from experience. We also develop methods to identify the visual features and representations that AI models use, and we apply computer vision where measurements would be impractical to collect by hand.

Human-AI Alignment in Vision

As recognition accuracy improves, agreement with human behavior and primate neural recordings can plateau or decline. Our paper on vision-model alignment reports that better performance does not necessarily produce a better model of vision. In our ClickMe game, people mark the parts of an image that help them recognize an object. We use them to train networks to rely on similar visual evidence, a procedure we call harmonization. This improves agreement with human vision without changing the network architecture. It gives us a way to investigate how visual experience and learning objectives shape a model's behavior.

In work on joint energy-based models of vision, we compare models trained to classify images with models that also learn the distribution of images themselves. We find that combining these discriminative and generative objectives can produce better agreement with human vision than either objective alone.
Funded by NSF (IIS-2402875) and featured by NSF.

Cognitive Benchmarks for Visual Reasoning

We design tasks that isolate specific demands on vision. The Pathfinder task tests whether a model can follow a contour through clutter. Our recurrent models succeed on conditions that challenge the tested feedforward networks.

The Compositional Visual Relations benchmark tests whether learned visual concepts can be combined in new ways. The 3D-PC benchmark asks models to reason about how a scene appears from another viewpoint. The 3D-PC project page, 3D-PC code, and 3D-PC dataset are available for that benchmark.

In same–different tasks, a model must judge whether two objects match, then apply that relation to objects it has not encountered during training. Comparing people and models on these tasks helps us distinguish successful recognition from the computations needed for grouping and relational reasoning.
Funded by ONR (N00014-24-1-2026).

Cortical Feedback and Recurrent Vision

The cortex has a limited number of processing stages. Feedback and horizontal connections add computation over time. We study how these interactions change a representation and what they contribute to perception.

Our perceptual-grouping paper links recurrent network dynamics to the time course of grouping in natural scenes. Experiments on same–different judgments reveal neural dynamics associated with relational processing.

In collaboration with David Sheinberg, our mental-simulation paper asks how monkeys predict where a ball will land after falling through obstacles, before seeing it move. Their behavior and eye movements are consistent with mentally tracing the ball's path. We compare these observations with recurrent networks that learn to solve the same task.

We also investigate whether synchronized neural activity helps track objects and solve feature binding: keeping the color, shape, and other properties of each object associated with that object rather than mixing them with those of its neighbors. To make these mechanisms practical at larger scales, we develop stable recurrent models and convolutional state space models.
Funded by ONR (N00014-24-1-2026) and ANITI (France 2030, ANR-23-IACL-0002).

Development and Embodiment

Animals learn to see through experience that unfolds continuously as they move through the world. We study what models can learn from that temporal structure, and what additional information comes from acting. We collaborate with developmental cognitive scientists to identify the learning principles that support perceptual development, including how intrinsic rewards guide exploration.

Ongoing work uses a newborn chick's first-person visual experience to test whether predictive learning can support grouping and object recognition without labels. Our visual perspective-taking experiments expose limitations of the tested models, motivating work on agents that learn through exploration. In collaboration with Fulvio Domini, we also study the computational mechanisms of depth perception.
Funded by the REPRISM MURI (ONR N00014-24-1-2603), ONR (N00014-24-1-2026), and the NSF AI Research Institute on Interaction for AI Assistants (ARIA; NSF Cooperative Agreement 2433429).

Explainable AI for Scientific Discovery

A model's predictions can be accurate for the wrong reasons. In our histopathology paper, models trained to predict tumor mutations from tissue images relied on correlations with tissue subtype and failed a more demanding test. Accurate predictions alone did not establish that the models measured the intended biology.

With collaborators at the Artificial and Natural Intelligence Toulouse Institute (ANITI), we develop methods to examine the evidence behind such predictions. CRAFT identifies concepts used by a model, and MACO visualizes its features. These methods are available through Xplique and Horama libraries.

We test explanations with people, finding that more capable vision models are not necessarily more interpretable and comparing which representations people can understand. Applications include identifying plant families from leaf architecture. LeafLENS and ObjectLENS let visitors explore model explanations directly.
Funded by ANITI (France 2030, ANR-23-IACL-0002) and the NSF AI Research Institute on Interaction for AI Assistants (ARIA; NSF Cooperative Agreement 2433429).

Foundation Models and Multimodal Reasoning

Foundation models offer increasingly powerful predictions of behavior and brain activity. Establishing what those predictions explain requires understanding how the models learn and represent information. Our perspective on prediction and understanding develops that distinction.

We ask how the training objective determines which information a model retains. Contrastive learning brings representations of matched inputs closer together while separating mismatched inputs. Predictive learning trains a model to infer one representation from another. Our spectral theory of multimodal losses studies the relationship between these objectives and the representations they produce. We also study shared multimodal workspaces, failures of feature binding in vision–language models, and language-model representations that track human judgments of event plausibility.
Funded by the NSF AI Research Institute on Interaction for AI Assistants (ARIA; NSF Cooperative Agreement 2433429) and ONR (N00014-24-1-2026).

AI for Behavioral and Clinical Science

Studying behavior often requires researchers to watch and label many hours of video. Our automated home-cage phenotyping paper describes a system that learns to recognize mouse behaviors such as eating, grooming, and resting, so researchers can measure when these behaviors occur and how they change over time. We have extended video-based behavioral analysis to worms, zebrafish, and children, and we have used these measurements to characterize mouse models of amyotrophic lateral sclerosis and frontotemporal dementia (ALS-FTD).
ALS-FTD phenotyping was supported by NIH/NINDS (R21 NS112743, 2020–2022).

In a separate collaboration with Taylor Burke at Massachusetts General Hospital, we develop computer-vision methods to analyze photographs of self-inflicted injuries. The models measure visible signs of injury severity. We are testing whether these measurements, combined with clinical assessments, can improve prediction of future suicide attempts. This work, supported by NIH award R01 MH140004, investigates a potential source of information for clinicians assessing suicide risk.
Suicide-risk research is funded by NIH/NIMH (R01 MH140004 and R01 MH143695).

With David Borton's group, we develop machine learning methods to personalize electrical stimulation after spinal cord injury. Our models learn how stimulation settings relate to muscle activity, helping the team find settings that produce a desired response without testing every possible combination. This work contributes to a neuroprosthetic interface that restored muscle control and sensory feedback in three participants with spinal cord injuries.
Spinal-interface research was supported by DARPA (D19AC00015, 2019–2024; PI: David Borton).

Teaching

I teach computational courses at the interface between natural and artificial intelligence, bridging neuroscience, cognitive science, and AI.

CPSY 1291: Computational Methods for Mind, Brain & Behavior
Advanced Undergraduate/Graduate • Fall Semester
A broad introduction to NeuroAI combining lectures with hands-on programming assignments. Students explore computational models of brain and cognition, classical machine learning algorithms, and modern deep learning architectures.

CPSY 1950: Deep Learning in Brains, Minds & Machines
Advanced Undergraduate/Graduate • Spring Semester
A seminar on how biological and artificial systems process information. Students read and discuss recent papers in NeuroAI and evaluate their methods and evidence.

Selected recent publications

See the lab publications page for a complete list of publications.


Selected talks

A selection of recorded talks. For the full list of invited talks, see my CV (PDF).

Scaling laws vs. neural laws: Toward more natural artificial vision
Keynote, IEEE CVPR 2026, Denver, CO • June 2026

Modern computer vision has advanced by scaling models, data and compute, and deep networks now match or exceed human accuracy on benchmarks such as ImageNet. Yet their alignment with human vision is fragile: on simple probes from the cognitive sciences, even the largest models fall to near chance, and models that reach human accuracy do so with very different visual strategies. This keynote argues that more natural artificial vision will come from the neural laws of biological vision: the developmental principles that shape how brains learn to see, and the recurrent architecture of cortex.

Cortical feedback mechanisms in visual reasoning: From perceptual grouping to abstract compositional reasoning
DIC-ISC-CRIA Seminar, Montreal • February 2026

How does cortical feedback contribute to visual reasoning? This talk examines perceptual grouping and same–different judgments, using computational models to test how recurrent processing helps solve these tasks.


Active grants