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.
- 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.
- Baker, P. M., & Bair, W. (2016). A model of binocular motion integration in MT neurons. Journal of Neuroscience, 36(24), 6563-6582.
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)
- '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.
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.
- 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.
Karl Gegenfurtner, Universität Giessen, works on on the relationship between low level sensory processes, higher level visual cognition, and sensorimotor integration.
- Flachot, A., Akbarinia, A., Schütt, H. H., Fleming, R. W., Wichmann, F. A., & Gegenfurtner, K. R. (2022). Deep neural models for color classification and color constancy. Journal of Vision, 22(4), 17.
- Witzel, C., & Gegenfurtner, K. R. (2018). Color perception: Objects, constancy, and categories. Annual Review of Vision Science, 4, 475-499.
- Koenderink, J., van Doorn, A., & Gegenfurtner, K. (2018). Color weight photometry. Vision Research, 151, 88-98.
- Hansen, T., Olkkonen, M., Walter, S. & Gegenfurtner, K.R. (2006) Memory modulates color appearance. Nature Neuroscience, 9, 1367-1368.
- Gegenfurtner, K.R. & Kiper, D.C. (2003) Color vision. Annual Review of Neuroscience, 26, 181-206.
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.
- 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.
- 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.
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.
- Trommershäuser, J., Maloney, L. T. & Landy M. S. (2008). Decision making, movement planning and statistical decision theory. Trends in Cognitive Science, 12, 291-297.
- Geisler, W.S. (1989). Sequential-ideal observer analysis of visual discriminations. Psychological Review, 96, 267-314.
- 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.
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]
- 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]
- Adelson EA & Bergen JR (1985). Spatiotemporal energy models for the perception of motion. J Opt Soc Am A. 2:284-99.
- 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]
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.
- 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.
- Parker, A. J. (2007). Binocular depth perception and the cerebral cortex. Nature Reviews Neuroscience, 8(5), 379-391.
- 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.
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.
- 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.
- Pasupathy, A., El-Shamayleh, Y., & Popovkina, D. V. (2018). Visual shape and object perception. In S. Murray Sherman (Ed.). Oxford research encyclopedia of neuroscience
- Kim, T., Bair, W., & Pasupathy, A. (2019). Neural coding for shape and texture in macaque area V4. Journal of Neuroscience, 39(24), 4760-4774.
- Oleskiw, T. D., Nowack, A., & Pasupathy, A. (2018). Joint coding of shape and blur in area V4. Nature communications, 9(1), 1-13.
- 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.
- Pasupathy, A., & Connor, C. E. (2002). Population coding of shape in area V4. Nature neuroscience, 5(12), 1332-1338.
- Pasupathy, A., & Connor, C. E. (2001). Shape representation in area V4: position-specific tuning for boundary conformation. Journal of neurophysiology, 86, 2505-2519
Rosanne Rademaker, Ernst Strüngmann Institute & Max Planck Society, studies how visual information can be held in mind in a robust & flexible way, particularly in light of bottom-up and top-down processes that draw simultaneously upon early visual cortical circuits (using human neuroimaging & psychophysics).
- Henderson, M.M., Rademaker, R.L., & Serences, J.T. (2022). Flexible utilization of spatial- and motor-based codes for the storage of visuo-spatial information. eLife, 11, e75688.
- Iamshchinina, P., Christophel, T.B., Gayet, S., & Rademaker, R.L. (2021). Essential considerations for exploring visual working memory storage in the human brain. Visual Cognition, 29(7), 425–436.
- Rademaker, R.L., Chunharas, C. & Serences, J.T. (2019). Coexisting representations of sensory and mnemonic information in human visual cortex. Nature Neuroscience, 22: 1336–1344.
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.
- 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.
- Ringach, D. L. (2019). The geometry of masking in neural populations. Nature communications, 10(1), 1-11.
- 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.
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.
- 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.
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.
- 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.
- 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.
- 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.
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)
- Treue, S. (2001). Neural correlates of attention in primate visual cortex.
Trends in Neurosciences, 24
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]