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 http://www.imodel.org).

• 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]
• Entezari S, Bair W (2019) Artiphysiology reveals visual preferences underlying V4-like blur selectivity in a deep convolutional neural network. [pdf]
• Kim T, Bair W, Pasupathy A (2019) Neural coding for shape and texture in macaque area V4. J Neurosci 39:4760-4774. [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]
• Oleskiw TD, Pasupathy A, Bair W (2014). Spectral receptive fields do not explain tuning for boundary curvature in V4 neurons. J Neurophysiol 112:2114-2122. [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 on the relationship between low level sensory processes, higher level visual cognition, and sensorimotor integration.

• Witzel, C., & Gegenfurtner, K. R. (2018). Color perception: Objects, constancy, and categories. Annual Review of Vision Science, 4, 475-499. [pdf]
• Gegenfurtner, K.R. & Kiper, D.C. (2003) Color vision. Annual Review of Neuroscience, 26, 181-206. [pdf]
• Gil Rodríguez, R., Hedjar, L., Toscani, M., Guarnera, D., Guarnera, G.C. & Gegenfurtner, K.R. (2024) Color Constancy mechanisms in virtual reality environments. Journal of Vision, 25(5), 6. [pdf]

Kalanit Grill-Spector, Stanford, studies high level vision using a combination of imaging techniques, behavioral measurements, and computational modeling. She examines how the functional neuroanatomy of visual cortex and computations by neural populations support efficient visual perception. In the course, she will discuss visual category representations in ventral temporal cortex (as well as the modular/distributed debate), how basic principles such as cytoarchitecture, white matter connections, and eccentricity biases constrain the functional organization of ventral temporal cortex, and how computations by population receptive fields explain perceptual phenomenon like the face inversion effect and simultaneous suppression.

• Grill-Spector, K., & Weiner, K. S. (2014). The functional architecture of the ventral temporal cortex and its role in categorization. Nature Reviews Neuroscience, 15(8), 536-548. [pdf]
• Grill-Spector, K., Weiner, K. S., Kay, K., & Gomez, J. (2017). The functional neuroanatomy of human face perception. Annual review of vision science, 3, 167. [pdf]
• Poltoratski, S., Kay, K., Finzi, D., & Grill-Spector, K. (2021). Holistic face recognition is an emergent phenomenon of spatial processing in face-selective regions. Nature communications, 12(1), 1-13. [pdf]

Zoe Kourtzi, University of Cambridge, focuses on imaging the neural processes in the human brain that mediate complex, adaptive cognitive functions and behaviour.

• Li, S., Mayhew, S. D., & Kourtzi, Z. (2009). Learning shapes the representation of behavioral choice in the human brain. Neuron 62, 441-452. [pdf]
  
• Li, S., Ostwald, D., Giese, M., & Kourtzi, Z. (2007). Flexible coding for categorical decisions in the human brain. J Neurosci. 27(45):12321-12330. [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]
• Mamassian, P. & Gardelle, V. de. (2022). Modeling perceptual confidence and the Confidence Forced-Choice paradigm. Psychological Review, 129(5), 976–998. [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]
• Lennie P, Movshon JA (2005). Coding of color and form in the geniculostriate visual pathway. J Opt Soc Am A 22: 2013-2033. [pdf]
• Roska B & Meister M (2014) The retina dissects the visual scene into distinct features. In The New Visual Neurosciences (Werner, JS, Chalupa, LM, eds), pp 163–182. Cambridge, MA: MIT Press. [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]
• Kim YJ, Peterson BB, Crook JD, Joo HR, Wu J, Puller C, Robinson FR, Gamlin PD, Yau K-W, Viana F, Troy JB, Smith RG, Packer OS, Detwiler PB, Dacey DM (2022). Origins of direction selectivity in primate retina. Nature Communications. (Supplement to figure 1 only) [pdf]

• Adelson EA & Bergen JR (1985). Spatiotemporal energy models for the perception of motion. J Opt Soc Am A. 2:284-99. [pdf]
• Emerson RC, Bergen JR, Adelson EH (1992). Directionally selective complex cells and the computation of motion energy in cat visual cortex. Vision Res. 32:203-18. [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]
• Manning T & Britten K (2017) Motion Processing in Primates (Oxford Encyclopedia of Neuroscience). [pdf]
• Wienecke, CF, Leong, JC, & Clandinin, TR (2018). Linear summation underlies direction selectivity in Drosophila. Neuron, 99(4), 680-688. [pdf]
• Vanni, S, Hokkanen, H, Werner, F, & Angelucci, A. (2020). Anatomy and physiology of macaque visual cortical areas V1, V2, and V5/MT: bases for biologically realistic models. Cerebral Cortex, 30(6), 3483-3517. [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., Popovkina, D. V., & Kim, T. (2020). Visual functions of primate area V4. Annual review of vision science, 6, 363. [pdf]
• Pasupathy, A., Kim, T., & Popovkina, D. V. (2019). Object shape and surface properties are jointly encoded in mid-level ventral visual cortex. Current opinion in neurobiology, 58, 199-208. [web]
• Pasupathy, A., El-Shamayleh, Y., & Popovkina, D. V. (2018). Visual shape and object perception. In S. Murray Sherman (Ed.). Oxford research encyclopedia of neuroscience [pdf]
• Kim, T., Bair, W., & Pasupathy, A. (2019). Neural coding for shape and texture in macaque area V4. Journal of Neuroscience, 39(24), 4760-4774. [pdf]
• Oleskiw, T. D., Nowack, A., & Pasupathy, A. (2018). Joint coding of shape and blur in area V4. Nature communications, 9(1), 1-13. [pdf]
• Fyall, A. M., El-Shamayleh, Y., Choi, H., Shea-Brown, E., & Pasupathy, A. (2017). Dynamic representation of partially occluded objects in primate prefrontal and visual cortex. Elife, 6, e25784. [pdf]
• Pasupathy, A., & Connor, C. E. (2002). Population coding of shape in area V4. Nature neuroscience, 5(12), 1332-1338. [pdf]
• Pasupathy, A., & Connor, C. E. (2001). Shape representation in area V4: position-specific tuning for boundary conformation. Journal of neurophysiology, 86, 2505-2519 [pdf]

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

• Rivlin-Etzion M., Grimes W. N. & Rieke F. (2018). Flexible Neural Hardware Supports Dynamic Computations in Retina. Trends in Neurosciences, 41 (4):224-237. [web]
• Warwick R. A., Kaushansky N., Sarid N., Golan A. & Rivlin-Etzion M. (2018). Inhomogeneous Encoding of the Visual Field in the Mouse Retina. Current biology, 28 (5):655-665. [pdf]
• Rivlin-Etzion M., Wei W. & Feller M. B. (2012). Visual Stimulation Reverses the Directional Preference of Direction-Selective Retinal Ganglion Cells. Neuron, 76 (3):518-525. [pdf]

Pieter Roelfsema, Netherlands Institute for Neurosciences, Amsterdam, is interested in how attentional processes coordinate neuronal activity in different brain areas (electrophysiology).

• Roelfsema, P. R., & Holtmaat, A. (2018). Control of synaptic plasticity in deep cortical networks. Nature Reviews Neuroscience, 19(3), 166. [pdf]
• van Vugt, B., Dagnino, B., Vartak, D., Safaai, H., Panzeri, S., Dehaene, S., & Roelfsema, P. R. (2018). The threshold for conscious report: Signal loss and response bias in visual and frontal cortex. Science, 360(6388), 537-542. [pdf]
• Mashour, G.A., Roelfsema, P.R. Changeux, J.-P. and Dehaene, S. (2020) Conscious processing and the global neuronal workspace hypothesis, Neuron 105, 776-798. [pdf]
• Chen, X. Wang, F., Fernandez, E. and Roelfsema, P.R. (2020) Shape perception via a high-channel-count neuroprosthesis in monkey visual cortex. Science 370, 1191-1196. [pdf]
• Kirchberger, L., Mukherjee, S., Schnabel, U.H., van Beest, E.H., Barsegyan, A., Levelt, C.N. Heimel, J.A., Lorteije, J.A.M., van der Togt, C., Self, M.W. and Roelfsema, P.R. (2021) The essential role of feedback processing for figure-ground perception in mice, Science Advances, 7, eabe1833. [pdf]
• Roelfsema, P.R. (2023) Solving the binding problem: assemblies form when neurons enhance their firing rate – they don’t need to oscillate or synchronize. Neuron, 111, 1003-1019. [pdf]

Ruth Rosenholtz, NVIDIA, works on behavioral experiments and computational modeling of human visual perception, and its applications. Particular vision topics include peripheral vision, visual attention, perceptual organization, material perception, and shape/depth perception. Applications include image quality, HCI, and vision for driving.

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

• 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]
• 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]
• Spering, M., Schütz, A. C., Braun, D.I., & Gegenfurtner, K. R. (2011). Keep your eyes on the ball: Smooth pursuit eye movements enhance prediction of visual motion. Journal of Neurophysiology, 105(4), 1756-1767. [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]
• Stewart, E. E. M., Valsecchi, M., & Schütz, A. C. (2020). A review of interactions between peripheral and foveal vision. Journal of Vision, 20(12):2, 1-35. [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]

Andrew Welchman, University of Birmingham, is interested in psychophysics and modelling of 3D vision, brain imaging and movement synchronisation.

• Ban H & Welchman AE (2015) fMRI analysis-by-synthesis reveals a dorsal hierarchy that extracts surface slant. Journal of Neuroscience, 35, 9823-35. [pdf]
• Goncalves NR, Ban H, Sánchez-Panchuelo RM, Francis ST, Schluppeck D & Welchman AE (2015) 7 tesla FMRI reveals systematic functional organization for binocular disparity in dorsal visual cortex. Journal of Neuroscience, 35, 3056-72. [pdf]
• Chang DHF, Mevorach C, Kourtzi Z & Welchman AE (2014) Training transfers the limits on perception from parietal to ventral cortex. Current Biology 24, 2445–2450. [pdf]
• Ban H, Preston TJ, Meeson A & Welchman AE (2012) The integration of motion and disparity cues to depth in dorsal visual cortex. Nature Neuroscience, 15, 636-43. [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]
• Wichmann, F. A., & Geirhos, R. (2023). Are deep neural networks adequate behavioural models of human visual perception? Annual Review of Vision Science, 9, 501–524 [pdf]