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home contact program alumni application

Organizers: Wolfgang Einhäuser-Treyer   Roland Fleming   Alexander Schütz
Funded by the Center for Mind, Brain and Behavior at
Justus-Liebig University Gießen and Philipps-University Marburg.

Preliminary program

Sunday 7
Arrival and welcome
20:00 Introduction ETFS
Monday 8
9:00-12:00 Lecture Tony Movshon Elements of vision
14:00-16:00 Poster session
16:00-19:00 Lecture Farran Briggs LGN
20:00 Discussion Tony Movshon Effective visual presentations
Tuesday 9
9:00-12:00 Lecture Michal Rivlin Retina
14:00-16:00 Exercise Wolfgang Einhäuser Natural scenes
16:00-19:00 Lecture Karl Gegenfurtner Color
20:00 Discussion Karl Gegenfurtner How to get your work published
Wednesday 10
9:00-12:00 Lecture Dario Ringach Cortex
14:00-16:00 Exercise Color
16:00-19:00 Lecture Tony Movshon Motion
Thursday 11
9:00-12:00 Lecture Andrew Parker Depth
14:00-17:00 Lecture Larry Maloney Perception and action
17:00-19:00 Exercise Larry Maloney Bayesian modeling
Friday 12
9:00-12:00 Lecture Felix Wichmann Deep learning
14:00-16:00 Exercise Felix Wichmann Deep learning
16:00-19:00 Lecture Roland Fleming Material perception
Saturday 13
9:00-12:00 Lecture Anitha Pasupathy Ventral cortex
14:00-17:00 Lecture Wyeth Bair Models of the ventral cortex
20:00 Banquet & Party
Sunday 14
Day off (optional trip to Marburg)
Monday 15
9:00-12:00 Lecture Kalanit Grill-Spector fMRI and functional specialization
14:00-16:00 Exercise Roland Fleming Computer graphics
16:00-19:00 Lecture Ruth Rosenholtz Understanding vision through crowding
20:00 Discussion Stefan Treue Animal research
Tuesday 16
9:00-12:00 Lecture Stefan Treue Physiology of attention
14:00-16:00 Poster session
16:00-19:00 Lecture James Bisley Eye movements and attention
20:00 Discussion Career planing
Wednesday 17
9:00-12:00 Lecture Alexander Schütz Eye movements and perception
14:00-16:00 Exercise Alexander Schütz Eye movement analysis
16:00-19:00 Lecture Holly Bridge Visual awareness
20:00 Feverish work on student projects
Thursday 18
9:00-12:00 Lecture Pascal Mamassian Confidence
14:00-17:00 Lecture Wolfgang Einhäuser Multistability
20:00 Student presentations
Friday 19
Farewell, transfer to airport

Daily meals

8-9: Breakfast
12-2: Lunch
7-8: Dinner

Confirmed speakers

Wyeth Bair, University of Washington, aims to understand neural circuitry and neural coding in the cerebral cortex of the primate visual system. He approaches this problem by recording directly from neurons in the functioning brain in vivo and by creating and refining large scale spiking neural network models that run on parallel computers (see

  • Pospisil, D. A., & Bair, W. (2021). The unbiased estimation of the fraction of variance explained by a model. PLoS computational biology, 17(8), e1009212. [pdf]
  • Pospisil, D. A., Pasupathy, A., & Bair, W. (2018). 'Artiphysiology'reveals V4-like shape tuning in a deep network trained for image classification. Elife, 7, e38242. [pdf]
  • Baker, P. M., & Bair, W. (2016). A model of binocular motion integration in MT neurons. Journal of Neuroscience, 36(24), 6563-6582. [pdf]

James Bisley, University of California, Los Angeles, studies the neuronal mechanisms underlying the allocation of visual attention and the guidance of eye movements.

  • Mirpour, K., & Bisley, J. W. (2021). The roles of the lateral intraparietal area and frontal eye field in guiding eye movements in free viewing search behavior. Journal of Neurophysiology, 125(6), 2144-2157. [pdf]
  • Bisley, J. W., & Mirpour, K. (2019). The neural instantiation of a priority map. Current opinion in psychology, 29, 108-112. [pdf]
  • Arcizet, F., Mirpour, K., Foster, D. J., & Bisley, J. W. (2018). Activity in LIP, but not V4, matches performance when attention is spread. Cerebral Cortex, 28(12), 4195-4209. [pdf]
  • Bisley, J. W., Goldberg, M. E. (2003). Neuronal activity in the lateral intraparietal area and spatial attention. Science 299:81-86. [pdf]

Holly Bridge, University of Oxford, aims to understand how the visual system can process input following the loss of V1 due to stroke or trauma. Using a combination of MRI approaches and behavioural testing her group is investigating the neural structures that may underlie any residual vision and how this vision could be improved.

  • Bridge, H. (2020). Loss of visual cortex and its consequences for residual vision. Current Opinion in Physiology, 16, 21-26. [pdf]
  • Ajina, S., Jünemann, K., Sahraie, A., & Bridge, H. (2021). Increased visual sensitivity and occipital activity in patients with hemianopia following vision rehabilitation. Journal of Neuroscience, 41(28), 5994-6005. [pdf]
  • Ajina, S., & Bridge, H. (2018). Blindsight relies on a functional connection between hMT+ and the lateral geniculate nucleus, not the pulvinar. PLoS biology, 16(7), e2005769. [pdf]
  • Ajina, S., & Bridge, H. (2017). Blindsight and unconscious vision: what they teach us about the human visual system. The Neuroscientist, 23(5), 529-541. [pdf]

Farran Briggs, University of Rochester, studies relationships between structure and function among neurons and circuits in the early visual system, with a focus on corticogeniculate feedback, and the role of visual attention in modulating activity in early visual circuits.

  • Hasse, JM, & Briggs, F (2017) Corticogeniculate feedback sharpens the temporal precision and spatial resolution of visual signals in the ferret. Proceedings of the National Academy of Sciences, 114(30), E6222-E6230. [pdf]
  • Murphy, AJ, Shaw, L, Hasse, JM, Goris, RL, & Briggs, F (2021) Optogenetic activation of corticogeniculate feedback stabilizes response gain and increases information coding in LGN neurons. Journal of Computational Neuroscience, 49(3), 259-271. [pdf]
  • Briggs, F (2020) Role of feedback connections in central visual processing. Annual Review of Vision Science, 6, 313-334. [pdf]

Wolfgang Einhäuser-Treyer, TU Chemnitz, works on attention and eye movements during natural-scene processing and in real-world tasks, and uses rivalry to study commonalities between perception, action and decision-making.

  • Einhäuser, W., Stout, J., Koch, C., & Carter, O. (2008). Pupil dilation reflects perceptual selection and predicts subsequent stability in perceptual rivalry. Proc Natl Acad Sci USA, 105(5) : 1704-1709. [pdf]
  • 't Hart, B.M., & Einhäuser, W. (2012). Mind the step: complementary effects of an implicit task on eye and head movements in real-life gaze allocation. Exp Brain Res, 223(2): 233-249. [pdf]

Roland Fleming, Universität Giessen, works on perception of shape, illumination and materials (psychophysics, computer graphics, modeling).

  • Fleming, R.W. (2014). Visual Perception of Materials and their Properties. Vision Research, 94, 62-75. [pdf]
  • Muryy, A., Welchman, A.E., Blake, A. and R.W. Fleming (2013). Specular reflections and the estimation of shape from binocular disparity. Proceedings of the National Academy of Sciences, 110(6): 2413-2418. [pdf]

Karl Gegenfurtner, Universität Giessen, works on color vision, natural images, and the relationship between perception and action (psychophysics).

  • Gegenfurtner, K.R., Bloj, M. & Toscani, M. (2015) The many colours of the dress. Current Biology, 25, R543-R544. [pdf]
  • Toscani, M., Valsecchi, M. & Gegenfurtner, K.R. (2013) Optimal sampling of visual information for lightness judgments. Proceedings of the National Academy of Sciences USA, 110(27), 11163-11168. [pdf]
  • Hansen, T., Olkkonen, M., Walter, S. & Gegenfurtner, K.R. (2006) Memory modulates color appearance. Nature Neuroscience,  9, 1367-1368. [pdf]
  • Gegenfurtner, K.R. & Kiper, D.C. (2003) Color vision. Annual Review of Neuroscience, 26, 181-206. [pdf]

Kalanit Grill-Spector, Stanford.

Larry Maloney, New York University, works on models of human performance based on mathematical statistics, physics and mathematics.

  • Ernst, M. O. & Bülthoff, H. H. (2004). Merging the senses into a robust percept. Trends in Cognitive Science, 8, 162-169. [pdf]
  • Trommershäuser, J., Maloney, L. T. & Landy M. S. (2008). Decision making, movement planning and statistical decision theory. Trends in Cognitive Science, 12, 291-297. [pdf]
  • Geisler, W.S. (1989). Sequential-ideal observer analysis of visual discriminations. Psychological Review, 96, 267-314. [pdf]
  • Landy, M. S., Maloney, L. T., Johnston, E. B., & Young, M. (1995). Measurement and modeling of depth cue combination: In defense of weak fusion. Vision Research, 35, 389-412. [pdf]

Pascal Mamassian, École Normale Supérieure, works on 3D, motion, and time perception, with an emphasis on sequential effects and confidence judgments.

  • Kiani, R., & Shadlen, M. N. (2009). Representation of confidence associated with a decision by neurons in the parietal cortex. Science, 324(5928), 759–764. [pdf]
  • Fleming, S. M., Weil, R. S., Nagy, Z., Dolan, R. J., & Rees, G. (2010). Relating introspective accuracy to individual differences in brain structure. Science, 329(5998), 1541–1543. [pdf]
  • Mamassian, P. (2016). Visual confidence. Annual Review of Vision Science, 2(1), 459–481. [pdf]

Tony Movshon, Center for Neural Science, New York, studies the function and development of the primate visual system, particularly the neurophysiological basis of motion perception (electrophysiology, psychophysics).

Elements of vision:

  • Marr DC (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information, chapter 1. MIT press. [pdf]
  • Enroth-Cugell C, Robson JG (1984). Functional Characteristics and Diversity of Cat Retinal Ganglion Cells. Investigative Ophthalmology and Visual Science 25: 250-267. [pdf]
  • Adelson EH, Bergen J (1991). The plenoptic function and the elements of early vision. In Computational Models of Visual Processing, Landy MS, Movshon JA, eds. MIT Press. [pdf]
  • Mante V, Frazor RA, Bonin V, Geisler WS, Carandini M (2005). Independence of luminance and contrast in natural scenes and in the early visual system. Nat Neurosci 8: 1690-1697. [pdf]
  • Lennie P, Movshon JA (2005). Coding of color and form in the geniculostriate visual pathway. J Opt Soc Am A 22: 2013-2033. [pdf]
  • Jazayeri, M, & Afraz, A (2017). Navigating the neural space in search of the neural code. Neuron, 93(5), 1003-1014. [pdf]
  • Krakauer, JW, Ghazanfar, AA, Gomez-Marin, A, MacIver, MA, & Poeppel, D (2017). Neuroscience needs behavior: correcting a reductionist bias. Neuron, 93(3), 480-490. [pdf]
  • Movshon JA, Simoncelli EP (2015). Representation of naturalistic image structure in the primate visual cortex. Cold Spring Harbor Symposia on Quantitative Biology 79: 115–122. [pdf]
  • Rust NC, Mante V, Simoncelli EP & Movshon JA (2006). How MT cells analyze the motion of visual patterns. Nature Neuroscience, 9(11), 1421-1431. [pdf]

Andrew Parker, University of Oxford and Otto-von-Guericke Universität Magdeburg. Andrew Parker’s research interests cover a wide range of topics in vision, with a particular emphasis on linking neuronal activity to perceptual judgments. Much of this work concerns the neurophysiological understanding of binocular depth and its relationship with other sources of information about three-dimensional shape.

  • Smith, J. E., & Parker, A. J. (2021). Correlated structure of neuronal firing in macaque visual cortex limits information for binocular depth discrimination. Journal of Neurophysiology, 126(1), 275-303. [pdf]
  • Parker, A. J. (2016). Vision in our three-dimensional world. Philosophical Transactions of the Royal Society B: Biological Sciences, 371(1697), 20150251. [pdf]
  • Read, J. C., Phillipson, G. P., Serrano-Pedraza, I., Milner, A. D., & Parker, A. J. (2010). Stereoscopic vision in the absence of the lateral occipital cortex. PloS one, 5(9), e12608. [pdf]
  • Parker, A. J. (2007). Binocular depth perception and the cerebral cortex. Nature Reviews Neuroscience, 8(5), 379-391. [pdf]
  • Cumming, B. G., & Parker, A. J. (1999). Binocular neurons in V1 of awake monkeys are selective for absolute, not relative, disparity. Journal of Neuroscience, 19(13), 5602-5618. [pdf]

Anitha Pasupathy, University of Washington, works on the neural basis of visual shape perception and recognition, the ability to identify and recognize objects from all angles, distances, and in almost any lighting condition. She uses single cell neurophysiological studies in awake monkeys, behavioral manipulations, computational modeling and reversible inactivation techniques to investigate how the information reaching our eyes is represented in the neural activity patterns in the brain, how these representations are transformed in successive stages and finally how these representations inform behavior.

  • Pasupathy, A., & Brincat, S. L. (2012). Population Coding of Object Contour Shape in V4 and Posterior Inferotemporal Cortex. In: Visual Population Codes: Toward a Common Multivariate Framework for Cell Recording and Functional Imaging, 189. [pdf]
  • Bushnell, B. N., Harding, P. J., Kosai, Y., & Pasupathy, A. (2011). Partial occlusion modulates contour-based shape encoding in primate area V4. The Journal of Neuroscience, 31(11), 4012-4024. [pdf]
  • Bushnell, B. N., & Pasupathy, A. (2012). Shape encoding consistency across colors in primate V4. Journal of Neurophysiology, 108(5), 1299-1308. [pdf]
  • Pasupathy, A., & Connor, C. E. (2002). Population coding of shape in area V4. Nature neuroscience, 5(12), 1332-1338. [pdf]

Dario Ringach, University of California, Los Angeles, studies the organization and function of primary visual cortex.

  • Ringach, D. L. (2021). Sparse thalamocortical convergence. Current Biology, 31(10), 2199-2202. [web]
  • Tring, E., & Ringach, D. L. (2018). On the Subspace Invariance of Population Responses. Neurons, Behavior, Data analysis, and Theory, 1(1), doi:10.1101/361568. [pdf]
  • Ringach, D. L. (2019). The geometry of masking in neural populations. Nature communications, 10(1), 1-11. [pdf]
  • Tan, L., Ringach, D. L., Zipursky, S. L., & Trachtenberg, J. T. (2021). Vision is required for the formation of binocular neurons prior to the classical critical period. Current Biology, 31(19), 4305-4313. [pdf]

Michal Rivlin, Weizmann Institute of Science, studies dynamic computations in retinal circuits and their mechanisms (electrophysiology, calcium imaging, modeling).

Ruth Rosenholtz, MIT, is interested in behavioral and computational aspects of human vision. Topics include peripheral vision, texture perception, perceptual organization, visual search, and scene perception. She also works on applied vision, most recently design of user interfaces and information visualizations, and vision for driving.

Alexander Schütz, University of Marburg, works on the relationship of eye movements and perception.

  • Schütz, A. C., Braun, D. I., & Gegenfurtner, K. R. (2011). Eye movements and perception: a selective review. Journal of Vision, 11(5):9, 1-30. [pdf]
  • Schütz, A. C., Braun, D. I., Kerzel, D., & Gegenfurtner, K. R. (2008). Improved visual sensitivity during smooth pursuit eye movements. Nature Neuroscience, 11(10), 1211-1216. [pdf]
  • Wolf, C., & Schütz, A. C. (2015). Trans-saccadic integration of peripheral and foveal feature information is close to optimal. Journal of Vision, 16(16):1, 1-18. [pdf]

Stefan Treue, German Primate Center Göttingen, works on the neural correlates of attention in primate visual cortex (electrophysiology, psychophysics, modeling).

  • Maunsell, J. H. R., & Treue, S. (2006). Feature-based attention in visual cortex. Trends in Neurosciences, 29(6) , 317-322. [pdf]
  • Treue, S. (2001). Neural correlates of attention in primate visual cortex. Trends in Neurosciences, 24 , 295-300. [pdf]

Felix Wichmann, Eberhard Karls Universität Tübingen, works on spatial vision, lightness- and brightness as well as object recognition, combining psychophysical experiments, computational modeling and machine learning..

  • Kriegeskorte, N. (2015). Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing. Annual Review of Vision Science, 1(1), 417–446. [pdf]
  • Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., & Wichmann, F. A. (2020). Shortcut learning in deep neural networks. Nature Machine Intelligence, 2(11), 665–673. [pdf]
  • Geirhos, R., Narayanappa, K., Mitzkus, B., Thieringer, T., Bethge, M., Wichmann, F. A., & Brendel, W. (2021). Partial success in closing the gap between human and machine vision. Advances in Neural Information Processing Systems (NeurIPS), 34. [pdf]