Papers and Links
|2/13/2013 ||Raif Rustamov ||Hyperalignment of Multi-Subject fMRI Data by Synchronized Projections || |
|12/12/2013 ||Chris Baldassano ||The Human Connectome Project ||The WU-Minn Human Connectome Project: An overview |
The Human Connectome Project: A data acquisition perspective
Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project
The minimal preprocessing pipelines for the Human Connectome Project
Full Publication List
|10/10/2013 ||Michelle Greene ||Towards Solving the Paradox of Scene Gist || |
|10/3/2013 ||Ben Poole ||Scene categories and object co-occurrences ||Stansbury, Naselaris, Gallant. Natural Scene Statistics Account for the Representation of Scene Categories in Human Visual Cortex |
|6/13/2013 ||Guest Speaker |
|Understanding rapid scene perception: From image statistics to scene gist via single-trial EEG responses || |
|6/6/2013 ||Cătălin Iordan ||Emulating human visual concept learning using a Bayesian machine vision system ||Work by Yangqing Jia, Joshua Abbott, Joseph Austerweil, Thomas Grifﬁths, and Trevor Darrell (paper under review) |
|5/23/2013 || ||VSS Debrief/Discussion || |
|5/2/2013 || |
|VSS Practice Talks: |
Discovering mental representations of complex natural scenes
Typicality Sharpens Object Representations in Object-Selective Cortex
Differential Connectivity Within the Parahippocampal Place Area
|4/25/2013 ||Chris Baldassano ||Discriminating stimuli using neuronal populations tuned away from relevant features ||The Serences Lab: |
Adaptive Allocation of Attentional Gain (2009);
Estimating the inﬂuence of attention on population codes in human visual cortex using voxel-based tuning functions (2009);
Basing Perceptual Decisions on the Most Informative Sensory Neurons (2010);
Optimal Deployment of Attentional Gain during Fine Discriminations (2012)
|4/4/2013 ||Michelle Greene ||Quantifying contextual information about objects || |
|2/28/2013 ||Michelle Greene ||Context in Rapid Scene Recognition || |
|2/21/2013 ||Ben Poole ||Comparing object representations in computer vision and the brain ||The Neural Representation Benchmark and its Evaluation on Brain and Machine. Charles F. Cadieu, Ha Hong, Dan Yamins, Nicolas Pinto, Najib J. Majaj, James J. DiCarlo |
|2/14/2013 ||Michelle Greene ||Causal Interactions between Scene-Sensitive Regions ||The Occipital Place Area Is Causally and Selectively Involved in Scene Perception |
|12/13/2012 ||Bertrand Thirion ||Spatial Regularization and Sparsity for Brain Mapping || |
|11/29/2012 ||Cătălin Iordan ||Discovering Voxel-Level Functional Connectivity Between Cortical Regions |
Natural Stimuli Acquire Basic-Level Advantage in Object-Selective Cortex
|11/8/2012 ||Abraham Botros |
|Wireless EEG Discussion || |
|11/1/2012 ||Michelle Greene |
OPAM Practice Talk
|Automatic basic-level object and scene categorization || |
|10/11/2012 ||Henryk Blasinski ||Hacking the brain with inexpensive EEG ||Ivan Martinovic, Doug Davies, Mario Frank, Daniele Perito, Tomas Ros and Dawn Song. "On the feasibility of side-channel attacks with Brain-Computer Interfaces". 21st Usenix Security Symposium (Usenix Security), August 2012. |
|10/4/2012 ||Cătălin Iordan ||Fine-Grained Visual Categorization || |
|9/20/2012 ||Abraham Botros ||Portable and Large-Scale EEG || Bobrov P, Frolov A, Cantor C, Fedulova I, Bakhnyan M, et al. (2011) Brain-Computer Interface Based on Generation of Visual Images. PLoS ONE 6(6): e20674. |
|8/23/2012 ||Chris Baldassano ||Practice Talk || |
|7/26/2012 ||Cătălin Iordan ||ROI Responses to Single Images ||Mur et al. (2012) Categorical, Yet Graded – Single-Image Activation Profiles of Human Category-Selective Cortical Regions |
|7/12/2012 ||Chris Baldassano ||Orientation Decoding: "Hyperacuity" and Directional bias || Kamitani & Tong (2005). Decoding the visual and subjective contents of the human brain |
Haynes & Rees (2005). Predicting the orientation of invisible stimuli from activity in human primary visual cortex.
Mannion et al (2009). Discrimination of the local orientation structure of spiral Glass patterns early in human visual cortex
Kriegeskorte et al (2009). How does an fMRI voxel sample the neuronal activity pattern: Compact-kernel or complex spatiotemporal ﬁlter?
Op de Beeck (2009). Against hyperacuity in brain reading: Spatial smoothing does not hurt multivariate fMRI analyses?
Kamitani & Sawahata (2009). Spatial smoothing hurts localization but not information: Pitfalls for brain mappers
Op de Beeck (2009). Probing the mysterious underpinnings of multi-voxel fMRI analyses
Swisher et al (2010). Multiscale Pattern Analysis of Orientation-Selective Activity in the Primary Visual Cortex
Freeman et al (2011). Orientation Decoding Depends on Maps, Not Columns.
Formisano & Kriegeskorte (2012). Seeing patterns through the hemodynamic veil — The future of pattern-information fMRI.
Swisher and Tong (2012). More than maps: the fMRI orientation signal persists after removal of radial bias
|7/5/2012 ||Michelle Greene ||Measuring Internal Representations from Behavioral and Brain Data ||Measuring Internal Representations from Behavioral and Brain Data. Marie L. Smith, Frédéric Gosselin, Philippe G. Schyns. Current Biology - 7 February 2012 |
|5/3/2012 ||VSS Practice Meeting |
Neural Representations of Object Categories at Multiple Taxonomic Levels
Neural Representation of Human-Object Interactions
|4/12/2012 ||Henryk Blasinski ||Sparse logistic regression for fMRI ||Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns. Okito Yamashita, Masa-aki Sato, Taku Yoshioka, Frank Tong, Yukiyasu Kamitani |
Perceptual Learning Incepted by Decoded fMRI Neurofeedback Without Stimulus Presentation. Kazuhisa Shibata, Takeo Watanabe, Yuka Sasaki, Mitsuo Kawato
|4/5/2012 ||Chris Baldassano ||MVPA and HMM ||Using brain imaging to track problem solving in a complex state space. John R. Anderson, Jon M. Fincham, Darryl W. Schneider, Jian Yang |
|2/16/2012 ||Cătălin Iordan ||Feed-forward semantic categorization ||First-Pass Selectivity for Semantic Categories in Human Anteroventral Temporal Lobe. Alexander M. Chan, Janet M. Baker, Emad Eskandar, Donald Schomer, Istvan Ulbert, Ksenija Marinkovic, Sydney S. Cash, and Eric Halgren |
|2/2/2012 ||Chris Baldassano ||"Hyperalignment" and between-subject classification ||A common, high-dimensional model of the representational space in human ventral temporal cortex. Haxby JV, Guntupalli JS, Connolly AC, Halchenko YO, Conroy BR, Gobbini MI, Hanke M, Ramadge PJ. Neuron. 2011 Oct 20;72(2):404-16. |
|12/8/2011 ||Logan Grosenick ||Interpretable multivariate models for fMRI regression and classification ||Grosenick et al. A family of interpretable multivariate models for regression and classification of whole-brain fMRI data |
|10/6/2011 ||Ben Poole ||Generating predicted responses to novel stimuli ||Mitchell et al. Predicting Human Brain Activity Associated with the Meanings of Nouns. |
|9/29/2011 ||Chris Baldassano ||Decoding scenes using objects ||Sean P MacEvoy & Russell A Epstein. Constructing scenes from objects in human occipitotemporal cortex |
|9/22/2011 ||Michelle Greene ||Neural representation of visual categories ||David J. Freedmana, Earl K. Miller. Neural mechanisms of visual categorization: Insights from neurophysiology |
David J. Freedman, Maximilian Riesenhuber, Tomaso Poggio, Earl K. Miller. Categorical Representation of Visual Stimuli in the Primate Prefrontal Cortex
|7/7/2011 ||Dave Taylor Jackson ||Visual Crowding and the Bouma Law ||Denis G Pelli and Katharine A Tillma. The uncrowded window of object recognition |
|6/9/2011 ||Chris Baldassano ||Object Categorization at Multiple Levels ||Mack et. al. Time course of visual object categorization: Fastest does not necessarily mean ﬁrst |
|4/28/2011 ||VSS Practice Meeting |
|Translation Invariance of Natural Scene Categories || |
|4/14/2011 ||Guest Speaker |
|Search for Functional Specificity in the Brain: A Nonparametric Hierarchical Bayesian Model for Group fMRI Data || |
|4/7/2011 ||Chris Baldassano ||Decoding visual stimuli from somatosensory cortex || Meyer et al. Seeing Touch Is Correlated with Content-Specific Activity in Primary Somatosensory Cortex. Cereb. Cortex (2011) |
|2/24/2011 ||Andrew Maas ||Modeling the activity of neuronal populations in macaque primary visual cortex ||http://www.nature.com/neuro/journal/v14/n2/full/nn.2733.html |
|2/17/2011 ||Andrew Saxe || Modeling sensory receptive field development with unsupervised feature learning || |
|2/10/2011 ||Cătălin Iordan ||Joint modeling of stimulus categories and voxel selectivities ||D. Lashkari, R. Sridharan, and P. Golland. Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations. NIPS 2010 |
|2/3/2011 ||Guest Speaker |
|The use of gist and context information to guide search in real world scenes || |
|1/13/2011 ||Chris Baldassano ||Relevance Vector Machines for simulantaneous EEG/fMRI ||De Martino, F., et al., Predicting EEG single trial responses with simultaneous fMRI and Relevance Vector Machine regression, NeuroImage (2010) |
|12/6/2010 ||Chris Baldassano ||fMRI alignment using spectral embedding ||Functional Geometry Alignment and Localization of Brain Areas. Georg Langs, Yanmei Tie, Laura Rigolo, Alexandra Golby, Polina Golland |
|12/2/2010 ||Kevin Leung ||Bayesian models for learning overhypotheses ||Kemp, C., Perfors, A. & Tenenbaum, J. B. (2007). Learning overhypotheses with hierarchical Bayesian models. Developmental Science, 10(3), 307-321. |
|11/4/2010 ||Cătălin Iordan ||Classifiers for decoding fMRI ||Masaya Misaki, Youn Kim, Peter A. Bandettini, Nikolaus Kriegeskorte, Comparison of multivariate classifiers and response normalizations for pattern-information fMRI, NeuroImage, Volume 53, Issue 1, 15 October 2010, Pages 103-118, ISSN 1053-8119, DOI: 10.1016/j.neuroimage.2010.05.051. |
|10/28/2010 ||Andrew Maas ||Reinforcement Learning ||Dayan P & Niv Y (2008) Reinforcement learning: The good, the bad and the ugly. Current Opinion in Neurobiology 18 185-196. |
|10/21/2010 ||Chris Baldassano ||Lie Detection using fMRI: Logistic Regression and SVM methods ||Langleben et al. Telling Truth From Lie in Individual Subjects With Fast Event-Related fMRI. Human Brain Mapping 26:262–272(2005) |
Davatzikos et al. Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection. NeuroImage 28 (2005) 663 – 668
|10/7/2010 ||Chris Baldassano ||Connectivity and Brain Maturity ||Nico U. F. Dosenbach, et al. Prediction of Individual Brain Maturity Using fMRI. Science. 2010;329(5997):1358. |
|9/30/2010 ||Andrew Saxe ||Development of Human Motor Maps ||Stoeckel MC, Seitz RJ, Buetefisch CM. Congenitally altered motor experience alters somatotopic organization of human primary motor cortex. Proceedings of the National Academy of Sciences of the United States of America. 2009;106(7):2395-400. |
|8/19/2010 ||Samir Menon ||Behavioral Maps in Motor Cortex || Michael Graziano. The Organization of Behavioral Repertoire in Motor Cortex. Annual Review of Neuroscience, Vol. 29: 105-134. |
|8/12/2010 ||Jim Lin ||Neural Coding for Visual Information ||Characterizing the Sparseness of Neural Codes, by B Willmore and D J Tolhurst |
Information Processing in Retina. Materials from Neuroscience, Fourth Edition
|8/5/2010 ||Chris Baldassano ||Optogenetics and fMRI ||Global and local fMRI signals driven by neurons defined optogenetically by type and wiring |
|7/22/2010 ||Andrew Maas ||Color Blindness and Neural Plasticity ||Gene therapy for red-green colour blindness in adult primates |
|7/15/2010 ||Brian Wandell and Anthony Sherbondy ||The visual pathways: Maps, plasticity, and reading || |
Visual Field Maps in Human Cortex B. A. Wandell, S.O. Dumoulin and A. A. Brewer (2007) Neuron, V. 56 , p. 366-383
Plasticity and stability of visual field maps in adult primary visual cortex. B. A. Wandell, S.M. Smirnakis (2009). Nature Reviews Neuroscience, doi:10.1038/nrn2741 N.B. The Supplementary Material is incorporated at the end of the PDF.
White matter pathways in reading. Michal Ben-Shachar, R.F. Dougherty and B.A. Wandell (2007). Current Opinions in Neurobiology Volume 17 pp. 258-270.
|7/8/2010 ||Koh Pang Wei ||Receptive Field Analysis ||Analyzing neural responses to natural signals: Maximally informative dimensions, by Sharpee et al. |
Adaptive filtering enhances information transmission in visual cortex, by Sharpee et al.
Cooperative Nonlinearities in Auditory Cortical Neurons, by Atencio et al.
|7/1/2010 ||Sophia Yang ||Decoding Mental Imagery ||Reddy L, Tsuchiya N, Serre T. Reading the mind's eye: decoding category information during mental imagery. Neuroimage. 2010 Apr 1;50(2):818-25. |
|6/24/2010 ||Dileep George ||Combining machine learning and neuroscience to build vision systems ||George D, Hawkins J, 2009 Towards a Mathematical Theory of Cortical Micro-circuits. PLoS Comput Biol 5(10) |
PhD thesis: "How the brain might work: A hierarchical temporal model for learning and recognition"
|6/17/2010 ||Stephen Baccus ||Computation and circuitry of object motion sensitivity in the retina ||S. Baccus, et al. A Retinal Circuit That Computes Object Motion. The Journal of Neuroscience 28(27): 6807-6817, 2008. |
B.P. Ölveczky, S.A. Baccus, and M. Meister. Segregation of object and background motion in the retina. Nature 423, 401-408, 2003.
|5/27/2010 ||Jay McClelland ||Semantic Cognition || McClelland, J. L., Rogers, T. T., Patterson, K., Dilkina, K. N., & Lambon Ralph, M. R. (2009). Semantic Cognition: Its Nature, Its Development, and its Neural Basis. In M. Gazzaniga (Ed.), The Cognitive Neurosciences IV. Boston, MA: MIT Press. Chapter 72. |
McClelland, J. L. (2009). The place of modeling in cognitive science. Topics in Cognitive Science, 1(1), 11-38.
|5/13/2010 ||Vidhya Navalpakkam ||Combining Economic and Visual Information in Decision-making ||1. L. Itti, C. Koch, A saliency-based search mechanism for overt and covert shifts of visual attention, Vision Research, Vol. 40, No. 10-12, pp. 1489-1506, May 2000. |
2. Platt, M.L. and Glimcher, P.W. (1999) Neural correlates of decision variables in parietal cortex. Nature. 400:233-238.
3. V. Navalpakkam, C. Koch, A. Rangel, P. Perona, Optimal reward harvesting in complex perceptual environments, In press: PNAS.
|4/29/2010 ||Kevin Leung ||Visual attention and pattern recognition || B. A. Olshausen, A. Anderson, and D. C. Van Essen. A neurobiological model of visual attention and pattern recognition based on dynamic routing of information. Journal of Neuroscience, 13(11):4700–4719, 1993. http://www.jneurosci.org/cgi/reprint/13/11/4700.pdf |
|4/15/2010 ||Ian Goodfellow ||Slow Feature Analysis || Franzius, Mathias and Sprekeler, Henning and Wiskott, Prof. Dr. Laurenz (2007) Slowness and Sparseness Lead to Place, Head-Direction, and Spatial-View Cells. |
Mathias Franzius, Niko Wilbert and Laurenz Wiskott. Invariant Object Recognition with Slow Feature Analysis.
|4/8/2010 ||Andrew Maas ||Whisker Sensory Pathway ||Main Paper: |
Neuronal encoding of texture in the whisker pathway. Arabzadeh E, Zorzin E, Diamond ME (2005)
Scholarpedia Vibrissal texture decoding
Classic Papers on Barrel Cortex:
Response properties of vibrissa units in rat SI somatosensory neocortex. Simons D. (1978)
Biometric analyses of vibrissal tactile discrimination in the rat. Simons D. (1990)
Neural computations in the retinal circuitry
Visual Parsing after Recovery from Blindness
|Ostrovsky et al. Psychological Science. 2009. Visual Parsing after Recovery from Blindness. |
fMRI Overview and Visual Decoding
Sharma J, Angelucci A, Sur M. Induction of visual orientation modules in auditory cortex. Nature. 2000;404(April):841-847.
von Melchner L, Pallas S, Sur M. Visual behaviour mediated by retinal projections directed to the auditory pathway. Nature. 2000;404(6780):871–876.