Publications

Collective Decision Making by Embodied Neural Agents
Nicolas Coucke
Mary Katherine Heinrich
Axel Cleeremans
Marco Dorigo
Guillaume Dumas
Collective decision making using simple social interactions has been studied in many types of multi-agent systems, including robot swarms an… (see more)d human social networks. However, existing multi-agent studies have rarely modeled the neural dynamics that underlie sensorimotor coordination in embodied biological agents. In this study, we investigated collective decisions that resulted from sensorimotor coordination among agents with simple neural dynamics. We equipped our agents with a model of minimal neural dynamics based on the coordination dynamics framework, and embedded them in an environment with a stimulus gradient. In our single-agent setup, the decision between two stimulus sources depends solely on the coordination of the agent's neural dynamics with its environment. In our multi-agent setup, that same decision also depends on the sensorimotor coordination between agents, via their simple social interactions. Our results show that the success of collective decisions depended on a balance of intra-agent, inter-agent, and agent-environment coupling, and we use these results to identify the influences of environmental factors on decision difficulty. More generally, our results demonstrate the impact of intra- and inter-brain coordination dynamics on collective behavior, can contribute to existing knowledge on the functional role of inter-agent synchrony, and are relevant to ongoing developments in neuro-AI and self-organized multi-agent systems.
2025-03-24
PNAS Nexus (published)
doi.org
arxiv.org
Data Visualization using Functional Data Analysis
Haozhe Chen
Andres Duque Correa
Guy Wolf
Kevin R. Moon
Data visualization via dimensionality reduction is an important tool in exploratory data analysis. However, when the data are noisy, many ex… (see more)isting methods fail to capture the underlying structure of the data. Furthermore, existing methods that can theoretically eliminate all noise are difficult to implement in high dimensions. Here we propose a new data visualization method called Functional Information Geometry (FIG) for dynamical processes that denoises the data by leveraging time information and mitigates the curse of dimensionality using approaches from functional data analysis. We experimentally demonstrate that FIG outperforms other methods in terms of capturing the true structure, hyperparameter robustness, and computational speed. We then use our method to visualize EEG brain measurements of sleep activity.
2025-03-24
SampTA/2025/Conference (poster)
doi.org
openreview.net
Extendable Planning via Multiscale Diffusion
Chang Chen
Hany Hamed
Doojin Baek
Taegu Kang
Yoshua Bengio
Sungjin Ahn
Long-horizon planning is crucial in complex environments, but diffusion-based planners like Diffuser are limited by the trajectory lengths o… (see more)bserved during training. This creates a dilemma: long trajectories are needed for effective planning, yet they degrade model performance. In this paper, we introduce this extendable long-horizon planning challenge and propose a two-phase solution. First, Progressive Trajectory Extension incrementally constructs longer trajectories through multi-round compositional stitching. Second, the Hierarchical Multiscale Diffuser enables efficient training and inference over long horizons by reasoning across temporal scales. To avoid the need for multiple separate models, we propose Adaptive Plan Pondering and the Recursive HM-Diffuser, which unify hierarchical planning within a single model. Experiments show our approach yields strong performance gains, advancing scalable and efficient decision-making over long-horizons.
2025-03-24
ArXiv (preprint)
doi.org
arxiv.org
Gemma 3 Technical Report
Gemma Team Aishwarya Kamath
Johan Ferret
Shreya Pathak
Nino Vieillard
Ramona Merhej
Sarah Perrin
Tatiana Matejovicova
Alexandre Ram'e
Morgane Rivière
Louis Rouillard
Thomas Mesnard
Geoffrey Cideron
Jean-Bastien Grill
Sabela Ramos
Edouard Yvinec
Michelle Casbon
Etienne Pot
Ivo Penchev
Gael Liu
Francesco Visin … (see 190 more)
Kathleen Kenealy
Lucas Beyer
Xiaohai Zhai
Anton Tsitsulin
Róbert Busa-Fekete
Alex Feng
Noveen Sachdeva
Benjamin Coleman
Yi Gao
Basil Mustafa
Iain Barr
Emilio Parisotto
David Tian
Matan Eyal
Colin Cherry
Jan-Thorsten Peter
Danila Sinopalnikov
Surya Bhupatiraju
Rishabh Agarwal
Mehran Kazemi
Dan Malkin
Ravin Kumar
David Vilar
Idan Brusilovsky
Jiaming Luo
Andreas Steiner
Abe Friesen
Abhanshu Sharma
Abheesht Sharma
Adi Mayrav Gilady
Adrian Goedeckemeyer
Alaa Saade
Alexander Kolesnikov
Alexei Bendebury
Alvin Abdagic
Amit Vadi
Andr'as Gyorgy
André Susano Pinto
Anil Das
Ankur Bapna
Antoine Miech
Antoine Yang
Antonia Paterson
Ashish Shenoy
Ayan Chakrabarti
Bilal Piot
Boxi Wu
Bobak Shahriari
Bryce Petrini
Charlie Chen
Charline Le Lan
Christopher A. Choquette-Choo
CJ Carey
Cormac Brick
Daniel Deutsch
Danielle Eisenbud
Dee Cattle
Derek Cheng
Dimitris Paparas
Divyashree Shivakumar Sreepathihalli
Doug Reid
Dustin Tran
Dustin Zelle
Eric Noland
Erwin Huizenga
Eugene Kharitonov
Frederick Liu
Gagik Amirkhanyan
Glenn Cameron
Hadi Hashemi
Hanna Klimczak-Pluci'nska
Harman Singh
Harsh Mehta
Harshal Tushar Lehri
Hussein Hazimeh
Ian Ballantyne
Idan Szpektor
Ivan Nardini
Jean Pouget-Abadie
Jetha Chan
Joe Stanton
J. Michael Wieting
Jonathan Lai
Jordi Orbay
Joe Fernandez
Joshua Newlan
Junsong Ji
Jyotinder Singh
Kat Black
Kathy Yu
Kevin Hui
Kiran N. Vodrahalli
Klaus Greff
Linhai Qiu
Marcella Valentine
Marina Coelho
Marvin Ritter
Matt Hoffman
Matthew Watson
Mayank Chaturvedi
Michael Moynihan
Min Ma
Nabila Babar
Natasha Noy
Nathan Byrd
Nick Roy
Nikola Momchev
Nilay Chauhan
Oskar Bunyan
Pankil Botarda
Paul Caron
Paul Kishan Rubenstein
Phil Culliton
Philipp Schmid
Pier Giuseppe Sessa
Pingmei Xu
Piotr Stańczyk
Pouya Dehghani Tafti
Rakesh Shivanna
Renjie Wu
Renke Pan
R. Rokni
Rob Willoughby
Rohith Vallu
Ryan Mullins
Sammy Jerome
Sara Smoot
Sertan Girgin
Shariq Iqbal
Shashir Reddy
Shruti Sheth
Siim Põder
Sijal Bhatnagar
S. Panyam
Sivan Eiger
Susan Zhang
Tianqi Liu
Trevor Yacovone
T. Liechty
Uday Kalra
Utku Evci
Vedant Misra
Vincent Roseberry
Vladimir Feinberg
Vlad Kolesnikov
Woohyun Han
Woosuk Kwon
X. T. Chen
Yinlam Chow
Yuvein Zhu
Zichuan Wei
Z. Egyed
Victor Cotruta
Minh Giang
Phoebe Kirk
Anand Rao
Jessica Lo
Erica Moreira
Luiz GUStavo Martins
Omar Sanseviero
Lucas Gonzalez
Zach Gleicher
Tris Brian Warkentin
Seyed Vahab Mirrokni
Evan Senter
Eli Collins
Joelle Barral
Zoubin Ghahramani
Raia Hadsell
Yossi Matias
D. Sculley
Slav Petrov
Noah Fiedel
Noam M. Shazeer
Oriol Vinyals
Jeffrey Dean
Demis Hassabis
Koray Kavukcuoglu
Clément Farabet
Elena Buchatskaya
Jean-Baptiste Alayrac
Rohan Anil
Dmitry Lepikhin
Sebastian Borgeaud
Olivier Bachem
Armand Joulin
Alek Andreev
Cassidy Hardin
Robert Dadashi
L'eonard Hussenot
We introduce Gemma 3, a multimodal addition to the Gemma family of lightweight open models, ranging in scale from 1 to 27 billion parameters… (see more). This version introduces vision understanding abilities, a wider coverage of languages and longer context - at least 128K tokens. We also change the architecture of the model to reduce the KV-cache memory that tends to explode with long context. This is achieved by increasing the ratio of local to global attention layers, and keeping the span on local attention short. The Gemma 3 models are trained with distillation and achieve superior performance to Gemma 2 for both pre-trained and instruction finetuned versions. In particular, our novel post-training recipe significantly improves the math, chat, instruction-following and multilingual abilities, making Gemma3-4B-IT competitive with Gemma2-27B-IT and Gemma3-27B-IT comparable to Gemini-1.5-Pro across benchmarks. We release all our models to the community.
2025-03-24
ArXiv (preprint)
doi.org
arxiv.org
MeshUp: Multi-Target Mesh Deformation via Blended Score Distillation
Hyunwoo Kim
Itai Lang
Noam Aigerman
Thibault Groueix
Vladimir Kim
Rana Hanocka
We propose MeshUp, a technique that deforms a 3D mesh towards multiple target concepts, and intuitively controls the region where each conce… (see more)pt is expressed. Conveniently, the concepts can be defined as either text queries, e.g.,"a dog"and"a turtle,"or inspirational images, and the local regions can be selected as any number of vertices on the mesh. We can effectively control the influence of the concepts and mix them together using a novel score distillation approach, referred to as the Blended Score Distillation (BSD). BSD operates on each attention layer of the denoising U-Net of a diffusion model as it extracts and injects the per-objective activations into a unified denoising pipeline from which the deformation gradients are calculated. To localize the expression of these activations, we create a probabilistic Region of Interest (ROI) map on the surface of the mesh, and turn it into 3D-consistent masks that we use to control the expression of these activations. We demonstrate the effectiveness of BSD empirically and show that it can deform various meshes towards multiple objectives. Our project page is at https://threedle.github.io/MeshUp.
2025-03-24
2025 International Conference on 3D Vision (3DV) (published)
doi.org
arxiv.org
A scalable gene network model of regulatory dynamics in single cells
Paul Bertin
Joseph D Viviano
Alejandro Tejada-Lapuerta
Weixu Wang
Stefan Bauer
Fabian J. Theis
Yoshua Bengio
2025-03-24
ArXiv (preprint)
doi.org
arxiv.org
General anaesthesia decreases the uniqueness of brain functional connectivity across individuals and species
Andrea I. Luppi
Daniel Golkowski
Andreas Ranft
Rudiger Ilg
Denis Jordan
Danilo Bzdok
Adrian M. Owen
Lorina Naci
Emmanuel A. Stamatakis
Enrico Amico
Bratislav Misic
The human brain is characterized by idiosyncratic patterns of spontaneous thought, rendering each brain uniquely identifiable from its neura… (see more)l activity. However, deep general anaesthesia suppresses subjective experience. Does it also suppress what makes each brain unique? Here we used functional MRI scans acquired under the effects of the general anaesthetics sevoflurane and propofol to determine whether anaesthetic-induced unconsciousness diminishes the uniqueness of the human brain, both with respect to the brains of other individuals and the brains of another species. Using functional connectivity, we report that under anaesthesia individual brains become less self-similar and less distinguishable from each other. Loss of distinctiveness is highly organized: it co-localizes with the archetypal sensory–association axis, correlating with genetic and morphometric markers of phylogenetic differences between humans and other primates. This effect is more evident at greater anaesthetic depths, reproducible across sevoflurane and propofol and reversed upon recovery. Providing convergent evidence, we show that anaesthesia shifts the functional connectivity of the human brain closer to the functional connectivity of the macaque brain in a low-dimensional space. Finally, anaesthesia diminishes the match between spontaneous brain activity and cognitive brain patterns aggregated from the Neurosynth meta-analytic engine. Collectively, the present results reveal that anaesthetized human brains are not only less distinguishable from each other, but also less distinguishable from the brains of other primates, with specifically human-expanded regions being the most affected by anaesthesia.
2025-03-23
Nature Human Behaviour (published)
doi.org
A transient neural code for feedback-driven motor corrections during reaching
Nina Kudryashova
Cole Hurwitz
Matthew G. Perich
Matthias H. Hennig
Movement is the result of complex, dynamic interaction between cortical and subcortical circuits. These dynamic interactions implement both … (see more)feedforward motor control, arising from preparatory states, and feedback control, triggered by unexpected sensory events during movement. We show that the neural responses for feedback-driven control can be transient and small in variance, posing difficulties for unsupervised inference methods. We thus propose the Behavior-Aligned Neural Dynamics (BAND) model, which exploits semi-supervised learning to extract latent dynamics that predict both feedforward planned movement and unplanned feedback corrections. Our analysis suggests that motor corrections during movement 1) are encoded on the population level in small neural variability in primary motor (M1), but not dorsal premotor (PMd) cortex; 2) are transient; and 3) are driven by sensory feedback. Our work highlights the importance of targeted closed-loop aware methods to extract and study neural dynamics underlying complex behavioral phenomena.
2025-03-23
bioRxiv (published)
doi.org
Offline Model-Based Optimization: Comprehensive Review
Minsu Kim
Jiayao Gu
Ye Yuan
Taeyoung YUN
Zixuan Liu
Yoshua Bengio
Can Chen
2025-03-20
ArXiv (preprint)
doi.org
arxiv.org
Quantifying associations between socio-spatial factors and cognitive development in the ABCD cohort.
Nicole Osayande
Justin Marotta
Shambhavi Aggarwal
Jakub Kopal
Avram J. Holmes
Sarah W. Yip
Danilo Bzdok
2025-03-19
Nature Computational Science (unknown)
doi.org
Quantifying associations between socio-spatial factors and cognitive development in the ABCD cohort
Nicole Osayande
Justin Marotta
Shambhavi Aggarwal
Jakub Kopal
Avram Holmes
Sarah W. Yip
Danilo Bzdok
2025-03-19
Nature Computational Science (published)
doi.org
Hitting the right pitch: Cortical tracking of speech fundamental frequency in auditory and somatomotor regions
Yorguin-Jose Mantilla-Ramos
Ana-Sofía Hincapié-Casas
Annalisa Pascarella
Tarek Lajnef
Richard M. Leahy
Emily B.J. Coffey
Karim Jerbi CoCo Lab
Véronique Boulenger
Low-frequency neural oscillations contribute to the parsing of continuous speech into linguistic units. Little is known however on the coupl… (see more)ing of brain rhythms to higher-frequencies in speech such as fundamental frequency (F0) or pitch. Using magnetoencephalography, we investigated whole-brain cortical tracking of F0 while participants listened to sentences produced at normal rate or fast rate, where pitch naturally increases, and to artificially accelerated sentences, where F0 remains unchanged. Our results revealed significant brain-to-F0 coupling across all speech rates not only in right auditory but also in right parietal, insular, and pre- and postcentral regions, likely including the ventral larynx area. Importantly, the cortico-acoustic coupling peak frequency was higher for natural fast speech to reflect the corresponding F0 increase compared to normal rate and time-compressed speech. These findings demonstrate the engagement of an auditory-somato-motor network in F0 tracking, supporting its role in facilitating phonemic processing during the perception of naturally-produced speech.
2025-03-17
bioRxiv (preprint)
doi.org