Work in progress

2. Beyond the feedforward sweep: feedback computations in the visual cortex

   The year in cognitive neuroscience

   G. Kreiman & T. Serre

3. Disentangling neural mechanisms for perceptual grouping

   ArXiv

   J.K. Kim*, D. Linsley*, K. Thakkar & T. Serre

4. Sample-efficient image segmentation through recurrence

   Arxiv

   D. Linsley*, J.K. Kim* & T. Serre

To appear

2. Deep learning: the good, the bad and the ugly

   Annual Review of Vision Science

   T. Serre & S. Leone

2019

2. Learning what and where to attend

   International Conference on Learning Representations

   D. Linsley, D. Schiebler, S. Eberhardt & T. Serre

2018

2. 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

3. Robust pose tracking with a joint model of appearance and shape

   Arxiv

   Y. Guo, L.N. Govindarajan, B. Kimia & T. Serre

4. Learning long-range spatial dependencies with horizontal gated-recurrent units

   Neural Information Processing Systems

   D. Linsley, J. Kim, V. Veerabadran, C. Windolf & T. Serre

5. Opponent surrounds explain diversity of contextual phenomena across visual modalities*

   Psychological Review

   D.A. Mely, D. Linsley & T. Serre

6. 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

7. Not-So-CLEVR: learning same–different relations strains feedforward neural networks

   Royal Society Interface Focus

   J.K Kim, M. Ricci & T. Serre

8. Same-different problems strain convolutional neural networks

   Annual Meeting of the Cognitive Science Society

   M. Ricci, J.K. Kim & T. Serre

9. 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

10. 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

2017

2. 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

2016

2. Models of visual categorization

   Wiley Interdisciplinary Reviews: Cognitive Science

   T. Serre

3. How deep is the feature analysis underlying rapid visual categorization?

   Neural Information Processing Systems

   S. Eberhardt, J. Cader & T. Serre

4. 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

5. Fast ventral stream neural activity enables rapid visual categorization

   Neuroimage

   M. Cauchoix*, S.M. Crouzet*, D. Fize & T. Serre

6. 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

7. Towards a theory of computation in the visual cortex

   Computational and Cognitive Neuroscience of Vision

   D. Mely & T. Serre

8. 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

9. 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

2015

2. Explaining the timing of natural scene understanding with a computational model of perceptual categorization

   PLoS Computational Biology

   I. Sofer, S. Crouzet & T. Serre

3. 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

4. Reduced expression of MYC increases longevity and enhances healthspan

   Cell

   J.W. Hofmann et al

2014

2. 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)

3. Neuronal synchrony in complex-valued deep networks

   International Conference on Learning Representations

   D. Reichert & T. Serre

4. 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

5. Hierarchical models of the visual system

   Encyclopedia of Computational Neuroscience

   T. Serre

6. 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

2013

2. 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

3. Models of the visual cortex

   Scholarpedia, 8(4):3516.

   T. Poggio & T. Serre

2012

2. 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

3. A new biologically inspired color image descriptor

   Proceedings of the European Computer Vision Conference

   J. Zhang, Y. Barhomi, T. Serre

4. 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

2011

2. What are the visual features underlying rapid recognition?

   Frontiers in Psychology

   S.M. Crouzet & T. Serre

3. 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

4. HMDB: A large video database for human motion recognition

   IEEE International Computer Vision Conference

   H. Kuhne, H. Jhuang, E. Garrote, T. Poggio, T. Serre

2010

2. Automated home-cage behavioral phenotyping of mice

   Nature Communications

   H. Jhuang, E. Garrote, X. Yu, V. Khilnani, T. Poggio, A. Steele, T. Serre

3. What and where: A Bayesian inference theory of attention

   Vision Research

   S. Chikkerur, T. Serre, C. Tan, T. Poggio

4. Elements for a neural theory of the processing of dynamic faces

   Dynamic Faces: Insights from Experiments and Computation

   T. Serre, M. Giese

5. Reading the mind’s eye: Decoding category information during mental imagery

   NeuroImage

   L. Reddy, N. Tsuchyia, T. Serre

6. 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

7. A neuromorphic approach to computer vision

   Communications of the ACM

   T. Serre & T. Poggio

2009

2. Attentive processing improves object recognition

   MIT Computer Science and Artificial Intelligence Laboratory Technical Report

   S. Chikkerur, T. Serre, T. Poggio

3. A Bayesian inference theory of attention: neuroscience and algorithms

   MIT Computer Science and Artificial Intelligence Laboratory Technical Report

   S. Chikkerur, T. Serre, T. Poggio

2007

2. 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

3. A feedforward architecture accounts for rapid categorization*

   Proceedings of the National Academy of Science

   T. Serre, A. Oliva, T. Poggio

4. 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

5. 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

6. 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

7. A component-based framework for face detection and identification

   International Journal of Computer Vision

   B. Heisele, T. Serre, T. Poggio

2006

2. 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

2005

2. 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

3. Learning features of intermediate complexity for the recognition of biological motion

   ICANN 2005

   R. Sigala, T. Serre, T. Poggio, M. Giese

4. Object recognition with features inspired by visual cortex

   IEEE Computer Vision and Pattern Recognition Conference

   T. Serre, L. Wolf, T. Poggio

5. Error weighted classifier combination for multi-modal human identification

   MIT Computer Science and Artificial Intelligence Laboratory Technical Report

   Y. Ivanov, T. Serre, J. Bouvrie

2004

2. 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

3. A new biologically motivated framework for robust object recognition

   MIT Computer Science and Artificial Intelligence Laboratory

   T. Serre, L. Wolf, T. Poggio

4. Using component features for face recognition

   International Conference on Automatic Face and Gesture Recognition

   Y. Ivanov, B. Heisele, T. Serre

2003

2. Hierarchical classification and feature reduction for fast face detection with support vector machines

   Pattern Recognition

   B. Heisele, T. Serre, S. Prentice, T. Poggio

2002

2. 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

3. Categorization by learning and combining object parts

   Advances in Neural Information Processing Systems

   B. Heisele, T. Serre, M. Pontil, T. Vetter, T. Poggio

2001

2. 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

3. Component-based face detection

   Computer Vision and Pattern Recognition

   B. Heisele, T. Serre, M. Pontil, T. Poggio

2000

2. Feature selection for face detection

   MIT Center for Biological and Computational Learning

   T. Serre, B. Heisele, S. Mukherjee, T. Poggio

"*" denotes supplementary information for the corresponding publication

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