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Stanford Cognition and Computation Reading Group


  • Time: 12:15PM on Thursdays

Upcoming Schedule:




Papers and Links

11/6/2014 Michelle Greene

Cătălin Iordan
Scene Categories Reflect Affordances

Category Cohesion and Distinctiveness in Human Visual Cortex Favor Basic Level Representations


Previous Speakers:





Papers and Links

9/18/2014 Chris Baldassano Brain Connectivity Conference Recap Conference Website
Presentation Slides (w/ paper links)
Speaker Slides
Alex Huth A probabilistic and generative model of cortical maps  

Michelle Greene
Cătălin Iordan
VSS Practice Talks: "Human estimates of object frequency are frequently over-estimated" and "Locally-Optimized Inter-Subject Alignment of Functional Cortical Regions"  

Ben Poole Comparing neural recordings and fMRI responses at high resolution Large-Scale, High-Resolution Neurophysiological Maps Underlying fMRI of Macaque Temporal Lobe
Chris Baldassano Effect of task on object representation Task context impacts visual object processing differentially across the cortex. 
Michelle Greene
Cătălin Iordan
Chris Baldassano
Recent paper "show and tell": HM, Baseball, FFA, and Object Recognition Postmortem examination of patient H.M.’s brain based on histological sectioning and digital 3D reconstruction

Improved vision and on-field performance in baseball through perceptual learning

Functional Subdomains within Human FFA

Resolving human object recognition in space and time
2/13/2014 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
Iris Groen
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
Michelle Greene
Cătălin Iordan
Chris Baldassano
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
w/ Michelle/Chris/Cătălin
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
Cătălin Iordan

Chris Baldassano

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
Cătălin Iordan
Translation Invariance of Natural Scene Categories

4/14/2011 Guest Speaker
Danial Lashkari
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
Michelle Greene
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)
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
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.
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 
Lab Website
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)  
Good Introduction:
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) 


Neda Nategh

Neural computations in the retinal circuitry

Gollisch T, Meister M, Neuron 2010. Eye smarter than scientists believed: neural computations in circuits of the retina


Dan O'Shea

Visual Parsing after Recovery from Blindness

Ostrovsky et al. Psychological Science. 2009. Visual Parsing after Recovery from Blindness.


Chris Baldassano

fMRI Overview and Visual Decoding

Main Paper: Identifying natural images from human brain activity Kendrick N. Kay, Thomas Naselaris, Ryan J. Prenger & Jack L. Gallant 

Other Papers:

Visual field representations and locations of visual areas V1/2/3 in human visual cortex, Dougherty et al. 2003

Detecting Awareness in the Vegetative State, Owen et al. 2006

Telling Truth From Lie in Individual Subjects With Fast Event-Related fMRI, Langleben 2005

In Vivo Assessment of Human Visual System Connectivity with Transcranial Electrical Stimulation during Functional Magnetic Resonance Imaging, Brandt et al. 2000

Exploring Functional Connectivity of the Human Brain using Multivariate Information Analysis B Chai, D Walther, D Beck, L Fei-Fei

Hierarchical Mixture of Classification Experts Uncovers Interactions between Brain Regions B Yao, D Walther, D Beck, L Fei-Fei 


Andrew Saxe

Neural Plasticity

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.

Moritz CT, Perlmutter SI, Fetz EE. Direct control of paralysed muscles by cortical neurons. Nature. 2008;456(December):639-643.



Group Description

  • To learn about the ways in which computer science and machine learning have been applied to better understand behavioral and physiological data, and to identify fields that could benefit from a computational approach
  • To introduce neuroscientists and psychologists to computational methods

Examples of topics that could be covered in this group include:
  • Cognitive Psychology
    • Modelling Learning (Tenenbaum, Nusbaum, Margoliash, Christiansen, Onnis, Hockema) 
    • Visual Attention (Navalpakkam, Itti, Baldi)
    • Neuroeconomics and optimality (Griffiths, Huettel, Loewenstein, Prelec)
  • Neuro-Physiology
    • fMRI of Vision (Engel, Wandell, Grill-Spector, Li)
    • fMRI of Motor Cortex (Milner, Park) 
    • EEG of Working Memory (Makeig, Hwang)
  • Computational Neuroscience
    • Motor System Learning (van Beers, Wolpert, Haggard, Mehring)
    • Spatial/Temporal Maps (Gerstner, Gallistel, Rolls)
    • Bayesian Models for Neocortex (Dean, Hawkins, George, Lee, Mumford)
  • Biologically Inspired Systems
    • Vision (Cox, Serre)
    • Slow feature analysis (Wiskott, Sejnowski, Berkes)
    • Neuromorphic Circuits (Boahen, Meshreki, Cymbalyuk)

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