- Discovering developmental mechanisms of memory-guided attention using computer vision
D. Amso, L. Govindarajan, P. Gupta, H. Baumgartner, A. Lynn, K. Gunther, D. Placido, T. Sharma, V. Veerabadran, K. Thakkar, S. Kim & T. Serre
- Differential involvement of EEG oscillatory components in identity vs. spatial-relation visual reasoning tasks
in submission
A. Alamia, C. Luo, M. Ricci, J. Kim, T. Serre & R. VanRullen
- Disentangling neural mechanisms for perceptual grouping
ArXiv
J.K. Kim*, D. Linsley*, K. Thakkar & T. Serre
- Sample-efficient image segmentation through recurrence
Arxiv
D. Linsley*, J.K. Kim* & T. Serre
Work in progress
- Development of a deep learning algorithm for the histopathologic diagnosis and gleason grading of prostate cancer biopsies: A pilot study
European urology focus
O. Kott*, D. Linsley*, A. Karagounis, C. Jeffers, G. Dragan, Ali Amin, T. Serre** & B. Gershman**
- Beyond the feedforward sweep: feedback computations in the visual cortex
The year in cognitive neuroscience
G. Kreiman & T. Serre
To appear
- Deep learning: the good, the bad and the ugly
Annual Review of Vision Science
T. Serre & S. Leone
- Learning what and where to attend
International Conference on Learning Representations
D. Linsley, D. Schiebler, S. Eberhardt & T. Serre
2019
- Early life stress leads to sex differences in development of depressive-like outcomes in a mouse model
Neuropsychopharmacology
H. Goodwill, G. Manzano-Nieves, M. Gallo, H.I. Lee, E. Oyerinde, T. Serre & K. Bath
- Robust pose tracking with a joint model of appearance and shape
Arxiv
Y. Guo, L.N. Govindarajan, B. Kimia & T. Serre
- Learning long-range spatial dependencies with horizontal gated-recurrent units
Neural Information Processing Systems
D. Linsley, J. Kim, V. Veerabadran, C. Windolf & T. Serre
- Complementary surrounds explain diversity of contextual phenomena across visual modalities*
Psychological Review
D.A. Mely, D. Linsley & T. Serre
- Neural computing on a raspberry pi: Applications to zebrafish behavior monitoring*
Visual observation and analysis of Vertebrate And Insect Behavior (VAIB)
L. Govindarajan, T. Sharma, R. Colwill & T. Serre
- Not-So-CLEVR: learning same–different relations strains feedforward neural networks
Royal Society Interface Focus
J.K Kim, M. Ricci & T. Serre
- Same-different problems strain convolutional neural networks
Annual Meeting of the Cognitive Science Society
M. Ricci, J.K. Kim & T. Serre
- TDP-43 gains function due to perturbed auto-regulation in a Tardbp knock-in mouse model of ALS-FTD
Nature Neuroscience
M.A. White et al
- Learning to predict action potentials end-to-end from calcium imaging data
IEEE Conference on Information Sciences and Systems
D Linsley, J Linsley, T Sharma, N Meyers & T Serre
2018
- What are the visual features underlying human versus machine vision?
IEEE ICCV Workshop on the Mutual Benefit of Cognitive and Computer Vision
D Linsley, S Eberhardt, T Sharma, P Gupta & T Serre
2017
- Models of visual categorization
Wiley Interdisciplinary Reviews: Cognitive Science
T. Serre
- How deep is the feature analysis underlying rapid visual categorization?
Neural Information Processing Systems
S. Eberhardt, J. Cader & T. Serre
- Computer vision cracks the leaf code
Proceedings of the National Academy of Sciences
P. Wilf, S. Zhang, S. Chikkerur, S. Little, S. Wing & T. Serre
- Fast ventral stream neural activity enables rapid visual categorization
Neuroimage
M. Cauchoix*, S.M. Crouzet*, D. Fize & T. Serre
- Source modelling of ElectroCorticoGraphy (ECoG) data: Analysis of stability and spatial filtering
Journal of Neuroscience Methods
A. Pascarella, C. Todaro, M. Clerc, T. Serre and M. Piana
- Towards a theory of computation in the visual cortex
Computational and Cognitive Neuroscience of Vision
D. Mely & T. Serre
- An end-to-end generative framework for video segmentation and recognition
IEEE Winter conference on Applications of Computer Vision
H. Kuehne, J. Galle & T. Serre
- A systematic comparison between visual cues for boundary detection
Vision Research (Special Issue on Vision and the Statistics of the Natural Environment)
D.A. Mély, J. Kim, M. McGill, Y. Guo and T. Serre
2016
- Explaining the timing of natural scene understanding with a computational model of perceptual categorization
PLoS Computational Biology
I. Sofer, S. Crouzet & T. Serre
- Unsupervised invariance learning of transformation sequences in a model of object recognition yields selectivity for non-accidental properties
Frontiers in Computational Neuroscience
S.M. Parker & T. Serre
- Reduced expression of MYC increases longevity and enhances healthspan
Cell
J.W. Hofmann et al
2015
- The neural dynamics of face detection in the wild revealed by MVPA
Journal of Neuroscience
M. Cauchoix^, G. Barragan-Jason^, T. Serre* & E.J. Barbeau* (^,* are authors with equal contributions)
- Neuronal synchrony in complex-valued deep networks
International Conference on Learning Representations
D. Reichert & T. Serre
- The language of actions: Recovering the syntax and semantics of goal-directed human activities
IEEE Conference on Computer Vision and Pattern Recognition
H. Kuehne, A. Arlsan & T. Serre
- Hierarchical models of the visual system
Encyclopedia of Computational Neuroscience
T. Serre
- Learning sparse prototypes for crowd perception via ensemble coding mechanisms
5th International Workshop on Human Behavior Understanding
Y. Zhang, S. Zhang, Q. Huang & T. Serre
2014
- Neural representation of action sequences: How far can a simple snippet-matching model take us?
Neural Information Processing Systems
C. Tan, J. Singer, T. Serre, D. Sheinberg & T. Poggio
- Models of the visual cortex
Scholarpedia, 8(4):3516.
T. Poggio & T. Serre
2013
- The ankyrin 3 (ANK3) bipolar disorder gene regulates mood-related behaviors that are modulated by lithium and stress*
Biological Psychiatry
M. Leussis, E. Berry-Scott, M. Saito, H. Jhuang, G. Haan, O. Alkan, C. Luce, J. Madison, P. Sklar, T. Serre, D. Root, T. Petryshen
- A new biologically inspired color image descriptor
Proceedings of the European Computer Vision Conference
J. Zhang, Y. Barhomi, T. Serre
- The neural dynamics of visual processing in monkey extrastriate cortex: A comparison between univariate and multivariate techniques
Neural Information Processing Systems – Workshop on Machine Learning and Interpretation in Neuroimaging
M. Cauchoix, A. Arslan, D. Fize, T. Serre
2012
- What are the visual features underlying rapid recognition?
Frontiers in Psychology
S.M. Crouzet & T. Serre
- Object decoding with attention in inferior temporal cortex
Proceedings of the National Academy of Sciences
Y. Zhang*, E. Meyers*, N. Bichot, T. Serre, T. Poggio, R. Desimone
- HMDB: A large video database for human motion recognition
IEEE International Computer Vision Conference
H. Kuhne, H. Jhuang, E. Garrote, T. Poggio, T. Serre
2011
- Automated home-cage behavioral phenotyping of mice
Nature Communications
H. Jhuang, E. Garrote, X. Yu, V. Khilnani, T. Poggio, A. Steele, T. Serre
- What and where: A Bayesian inference theory of attention
Vision Research
S. Chikkerur, T. Serre, C. Tan, T. Poggio
- Elements for a neural theory of the processing of dynamic faces
Dynamic Faces: Insights from Experiments and Computation
T. Serre, M. Giese
- Reading the mind’s eye: Decoding category information during mental imagery
NeuroImage
L. Reddy, N. Tsuchyia, T. Serre
- The story of a single cell: Peeking into the semantics of spikes
IAPR Workshop on Cognitive Information Processing
R. Kliper, T. Serre, D. Weinshall, I. Nelken
- A neuromorphic approach to computer vision
Communications of the ACM
T. Serre & T. Poggio
2010
- Attentive processing improves object recognition
MIT Computer Science and Artificial Intelligence Laboratory Technical Report
S. Chikkerur, T. Serre, T. Poggio
- A Bayesian inference theory of attention: neuroscience and algorithms
MIT Computer Science and Artificial Intelligence Laboratory Technical Report
S. Chikkerur, T. Serre, T. Poggio
2009
- Robust object recognition with cortex-like mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber & T. Poggio
- A feedforward architecture accounts for rapid categorization*
Proceedings of the National Academy of Science
T. Serre, A. Oliva, T. Poggio
- A quantitative theory of immediate visual recognition
Progress in Brain Research, Computational Neuroscience: Theoretical Insights into Brain Function
T. Serre, G. Kreiman, M. Kouh, C. Cadieu, U. Knoblich, T. Poggio
- Learning complex cell invariance from natural videos: a plausibility proof
MIT Computer Science and Artificial Intelligence Laboratory Technical Report
T. Masquelier, T. Serre, S. Thorpe, T. Poggio
- A biologically inspired system for action recognition
Proceedings of the Eleventh IEEE International Conference on Computer Vision
H. Jhuang, T. Serre, L. Wolf, T. Poggio
- A component-based framework for face detection and identification
International Journal of Computer Vision
B. Heisele, T. Serre, T. Poggio
2007
- Learning a dictionary of shape-components in visual cortex: Comparison with neurons, humans and machines
MIT Computer Science and Artificial Intelligence Laboratory Technical Report
T. Serre
2006
- A theory of object recognition: computations and circuits in the feedforward path of the ventral stream in primate visual cortex
MIT Computer Science and Artificial Intelligence Laboratory
T. Serre, M. Kouh, C. Cadieu, U. Knoblich, G. Kreiman, T. Poggio
- Learning features of intermediate complexity for the recognition of biological motion
ICANN 2005
R. Sigala, T. Serre, T. Poggio, M. Giese
- Object recognition with features inspired by visual cortex
IEEE Computer Vision and Pattern Recognition Conference
T. Serre, L. Wolf, T. Poggio
- Error weighted classifier combination for multi-modal human identification
MIT Computer Science and Artificial Intelligence Laboratory Technical Report
Y. Ivanov, T. Serre, J. Bouvrie
2005
- Realistic modeling of simple and complex cell tuning in the HMAX model, and implications for invariant object recognition in cortex
MIT Computer Science and Artificial Intelligence Laboratory
T. Serre, M. Riesenhuber
- A new biologically motivated framework for robust object recognition
MIT Computer Science and Artificial Intelligence Laboratory
T. Serre, L. Wolf, T. Poggio
- Using component features for face recognition
International Conference on Automatic Face and Gesture Recognition
Y. Ivanov, B. Heisele, T. Serre
2004
- Hierarchical classification and feature reduction for fast face detection with support vector machines
Pattern Recognition
B. Heisele, T. Serre, S. Prentice, T. Poggio
2003
- On the role of object-specific features for real-world object recognition in biological vision
Workshop on Biologically Motivated Computer Vision
T. Serre, J. Louie, M. Riesenhuber, T. Poggio
- Categorization by learning and combining object parts
Advances in Neural Information Processing Systems
B. Heisele, T. Serre, M. Pontil, T. Vetter, T. Poggio
2002
- Feature reduction and hierarchy of classifiers for fast object detection in video images
Computer Vision and Pattern Recognition
B. Heisele, T. Serre, S. Mukherjee, T. Poggio
- Component-based face detection
Computer Vision and Pattern Recognition
B. Heisele, T. Serre, M. Pontil, T. Poggio
2001
- Feature selection for face detection
MIT Center for Biological and Computational Learning
T. Serre, B. Heisele, S. Mukherjee, T. Poggio
2000
"*" denotes supplementary information for the corresponding publication
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