# Slides and readings ## EJ Chichilnisky ### Suggested Readings • Pillow, J.W., Shlens, J., Paninski, L., Sher, A., Litke, A.M., Chichilnisky, E.J. & Simoncelli, E.P. (2008). Spatio-temporal correlations and visual signalling in a complete neuronal population\ • Muratore, D.G. & Chichilnisky, E.J. (2019). Artificial Retina: A Future Cellular-Resolution Brain-Machine Interface \ • Shah, N.P., Brackbill, N., Rhoades, C., Kling, A., Goetz, G., Litke, A.M., Sher, A., Simoncelli, E.P. & A. Chichilnisky, E.J. (2020). Inference of nonlinear receptive field subunits with spike-triggered clustering
## John Serences Slides ### Suggested Readings • Sheehan, T.C. & Serences, J.T. (2022). Attractive serial dependence overcomes repulsive neuronal adaptation. PLoS Biology \ • Henderson, M.M., Serences, J.,T. & Rungratsameetaweemana, N. (2023). Dynamic categorization rules alter representations in human visual cortex\ • 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
## Eero Simoncelli • Slides part 1\ • Slides part 2 ### Suggested Readings • Barlow, H. B. (1961a). Possible principles underlying the transformations of sensory messages\ • Barlow, H. B. (1969). Pattern recognition and the responses of sensory neurons\ • Adelson, E. H. & Bergen J. R. (1991). The Plenoptic Function and the Elements of Early Vision\ • Wandell, B.A. (1995). Foundations of vision. [Chapter on trichromacy and metamers] \ • Balas, B., Nakano, L. & Rosenholtz, R. (2009). A summary-statistic representation in peripheral vision explains visual crowding\ • Freeman, J. & Simoncelli, E.P. (2011). Metamers of the ventral stream\ • Ziemba, C.M. & Simoncelli, E.P. (2021). Opposing effects of selectivity and invariance in peripheral vision\ • Zhou, J.Y. Duong, L.R. & Simoncelli, E.P. (2024). A unified framework for perceived magnitude and discriminability of sensory stimuli\ • Ganguli, D. & Simoncelli, E.P. (2016). Neural and perceptual signatures of efficient sensory coding\ • Wei, X-X., & Stocker, A.A. (2017). Lawful relation between perceptual bias and discriminability\ • Berardino, A., Laparra, V., Ballé, J. & Simoncelli, E.P. (2017). Eigen-distortions of hierarchical representations **Code repository** Eero's lab has produced a python library of tools to explore vision models by synthesizing novel informative images. This includes Metamers, Eigendistortions (Berardino 2017), Maximal differentiation (MAD) competition (Wang 2008), and Geodesics (Henaff 2016). https://github.com/LabForComputationalVision/plenoptic/
## Stephanie Palmer • Slides part 1\ • Slides part 2 • 1. Efficient coding notes\ • 2. Spike train information notes\ • 3. Probability and Inference ### Suggested Readings • Palmer, S.E., Marre, O., Berry, M.J. & Bialek, W. (2015). Predictive information in a sensory population\ • Salisbury, J.M. & Palmer, S.E. (2016). Optimal prediction in the retina and natural motion statistics\ • Wang, S., Segev, I., Borst, A. & Palmer, S. (2021). Maximally efficient prediction in the early fly visual system may support evasive flight maneuvers **Extra:** \ • Wang, S., Hoshal, B., de Laittre, E., Marre, O., Berry, M. & Palmer, S. (2022). Learning low-dimensional generalizable natural features from retina using a U-net.\ • Hoshal, B.D., Holmes, C.M., Bojanek, K., Salisbury, J., Berry, M.J., Marre, O. & Palmer, S.E. (2023). Stimulus invariant aspects of the retinal code drive discriminability of natural scenes
## Jonathan Pillow • Slides\ • GLM Tutorial (Matlab)\ • GLM Tutorial (Python) ### Suggested Readings • Pillow, Shlens, Paninski, Sher, Litke, Chichilnisky & Simoncelli (2008). Spatio-temporal correlations and visual signaling in a complete neuronal population\ • Park, Meister, Huk & Pillow (2014). Encoding and decoding in parietal cortex during sensorimotor decision-making\ • Weber & Pillow (2017). Capturing the dynamical repertoire of single neurons with generalized linear models.\ • Latimer, Rieke & Pillow (2019). Inferring synaptic inputs from spikes with a conductance-based neural encoding model
## Jacob Yates • Slides ### Suggested Readings • Casile A, Victor, J.D. & Rucci, M. (2019). Contrast sensitivity reveals an oculomotor strategy for temporally encoding space.\ • Victor, J.D., & Rucci M. (2015). The unsteady eye: an information-processing stage, not a bug\ • Yates, J.L. et al. (2023). Detailed characterization of neural selectivity in free viewing primates
## Lea Duncker • Slides ### Suggested Readings • Adelson, E.H. (2000). Lightness Perception and Lightness Illusions. (Chapter in The New Cognitive Neurosciences). \ • Bishop, C.M. (2006) Mixture Models and EM. (Chapter 9 in Pattern Recognition and Machine Learning). \ • Cunningham, J.P. & Yu, B.M. (2014). Dimensionality reduction for large-scale neural recordings. \ • Ernst M.O. & Banks, M.S. (2002). Humans integrate visual and haptic information in a statistically optimal fashion. \ • Olshausen, B.A. & Field, D.J. (1996). of simple-cell receptive field properties by learning a sparse code for natural Images.
## Ruth Rosenholtz • Slides ### Suggested Readings • Rosenholtz R. (2016). Capabilities and Limitations of Peripheral Vision \ • Rosenholtz, R., Huang, J. & Ehinger, K.A. (2012). Rethinking the Role of Top-Down Attention in Vision: Effects Attributable to a Lossy Representation in Peripheral Vision \ • Rosenholtz, R., Yu, D., & Keshvari, S. (2019). Challenges to pooling models of crowding: Implications for visual mechanisms
## Rachel Denison • Slides ### Suggested Readings **Recent reviews on dynamic attention:**\ • Denison, R.N. (2024). Visual temporal attention from perception to computation \ • Nobre, A.C. & van Ede, F. (2023). Attention in flux **If further interested in our normalization model of dynamic attention:**\ • Denison, R.N., Carrasco, M. & Heeger, D.J. A dynamic normalization model of temporal attention \ • Corresponding Code
## Tony Movshon • Slides ### Suggested Readings • Vanni et al (2020). Anatomy and Physiology of Macaque Visual Cortical Areas V1, V2, and V5/MT: Bases for Biologically Realistic Models\ • Adelson & Bergen (1985). Spatiotemporal energy models for the perception of motion\ • Emerson, Bergen & Adelson (1991). Directionally Selective and the Computation Cat Visual Cortex \ • Wei (2018). Neural Mechanisms of Motion Processing in the Mammalian Retina \ • Borst & Groschner (2023). How Flies See Motion \ • Wienecke, Leong & Clandinin (2018). Linear Summation Underlies Direction Selectivity in Drosophila \ • Rust et al. (2006). How MT cells analyze the motion of visual patterns
## Kate Bonnen • Slides ### Suggested Readings • Burge & Bonnen. (2024). Continuous psychophysics: Past, Present, and Future\ • Muller et al. (2023). Retinal motion statistics during natural locomotion\ • Bonnen, K., Burge, J., Yates, J., Pillow, J., & Cormack, L.K. (2015). Continuous psychophysics: Target-tracking to measure visual sensitivity\ • Noel, J.P., Caziot, B., Bruni, S., Fitzgerald, N.E., Avila, E., & Angelaki, D.E. (2021). Supporting generalization in non-human primate behavior by tapping into structural knowledge: Examples from sensorimotor mappings, inference, and decision-making\ • Matthis, J.S., Yates, J.L., & Hayhoe, M.M. (2018). Gaze and the control of foot placement when walking in natural terrain\ • Bonnen, K., Matthis, J.S., Gibaldi, A., Banks, M.S., Levi, D.M., & Hayhoe, M. (2021). Binocular vision and the control of foot placement during walking in natural terrain.
## Stefan Treue • Slides ### Suggested Readings
## Emily Cooper • Slides ### Suggested Readings • Dayan & Abbott Theoretical Neuroscience textbook population decoding chapter \ • Manning et al. (2024). Transformations of sensory information in the brain suggest changing criteria for optimality. \ • Sprague et al. (2015). Stereopsis is adaptive for the natural environment.
## Marlene Cohen • Slides ### Suggested Readings **This one first:** • Rust & Cohen (2022). Priority coding in the visual system\ **See also:** • Cohen & Kohn (2011). Measuring and interpreting neuronal correlations I will also touch on some newer methods for analyzing neural population data. People wanting to learn more might find these papers useful: • Sizemore, A.E., Phillips-Cremins, J.E., Ghrist, R. & Bassett, D.S. (2019). The importance of the whole: topological data analysis for the network neuroscientist\ • Rouse T.C., Ni A.M., Huang C & Cohen M.R. (2021). Topological insights into the neural basis of flexible behavior\ • Wu-Yan, E., Betzel, R.F., Tang, E., Gu, S., Pasqualetti, F. & Bassett, D.S. (2020). Benchmarking measures of network controllability on canonical graph models
## Madineh Sedigh-Sarvestani • Slides ### Suggested Readings • Clippingdale, S. & Wilson, R. (1996). Self-similar neural networks based on a Kohonen learning rule.\ • Konkle, T. & Alvarez, G.A. (2022) A self-supervised domain-general learning framework for human ventral stream representation.\ • Sedigh-Sarvestani et al. (2021). A sinusoidal transformation of the visual field is the basis for periodic maps in area V2.
## Ione Fine • Slides ### Suggested Readings • Fine, I. & Boynton, G.M. (2015). Pulse trains to percepts: the challenge of creating a perceptually intelligible world with sigh recovery technologies\ • Fine, I. & Boynton, G.M. (2023). A virtual patient simulation modeling the neural and perceptual effects of human visual cortical stimulation, from pulse trains to percepts
## Emma Alexander • Slides ### Suggested Readings • Introductory slides\ • Adelson, Edward H., and James R. Bergen. The plenoptic function and the elements of early vision. Vol. 2. Cambridge, MA, USA: Vision and Modeling Group, Media Laboratory, Massachusetts Institute of Technology, 1991\ • Liang, Chia-Kai, Yi-Chang Shih, and Homer H. Chen. "Light field analysis for modeling image formation." IEEE Transactions on Image Processing 20.2 (2010): 446-460
## Jennifer Groh • Slides ### Suggested Readings • Groh, J.M., Schmehl, M.N., Caruso V.C., & Tokdar, S.C. (2024). Signal switching may enhance processing power of the brain.\ • Jun, N.Y. et al. (2022). Coordinated multiplexing of information about separate objects in visual cortex. And optionally... • Schmehl, M.N. et al. (2024). Multiple objects evoke fluctuating responses in several regions of the visual pathway.\ • Caruso et al. (2018). Single neurons may encode simultaneous stimuli by switching between activity patterns
## Taraz Lee ### Suggested Readings • Lee, T., Sellers, J., Jonides, J. & Zhang, H. (2023). The forced-response method: A new chronometric approach to measure conflict processing\ • Pélisson, D., Alahyane, N., Panouillères, M. & Tilikete, C. (2010). Sensorimotor adaptation of saccadic eye movements.\ • Polanía, R., Nitsche, M.A. & Ruff, C.C. (2018). Studying and modifying brain function with non-invasive brain stimulation.\ • Theeuwes, J., Belopolsky, A. & Olivers, C.N.L. (2009). Interactions between working memory, attention, and eye movements.\ • van Ede, F. (2020). Visual working memory and action: Functional links and bi-directional influences.
## Mariam Aly • Slides part 1\ • Slides part 2 ### Suggested Readings • Aly M. & Turk-Browne N.B. (2018). Flexible weighting of diverse inputs makes hippocampal function malleable. \ • Turk-Browne N.B. (2019). The hippocampus as a visual area organized by space and time: A spatiotemporal similarity hypothesis.
## Kohitij Kar • Slides ### Suggested Readings Please read in order of priority: • Priority 1: Kar, K. & DiCarlo, J.J. (2024) The Quest for an Integrated Set of Neural Mechanisms Underlying Object Recognition in Primates. \ • Priority 2: Kar, K. (2019). Kubilius, J., Schmidt, K. Issa, E.B., DiCarlo, J.J. (2019). Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior. \ • Priority 3: Kar, K. & DiCarlo, J.J. (2021) Fast Recurrent Processing via Ventrolateral Prefrontal Cortex Is Needed by the Primate Ventral Stream for Robust Core Visual Object Recognition. \ • Priority 4: Tuckute, G., Finzi, D., Margalit, E., Yates, J., Zylberberg, J., Fyshe, A., Chung, S., Fedorenko, E., Kriegeskorte, N., Grill-Spector, K., & Kar, K. (2024). How to optimize neuroscience data utilization and experiment design for advancing primate visual and linguistic brain models?
## Lindsey Glickfeld • Slides part 1 (Normalization) • Slides part 2 (Data) ### Suggested Readings • Barbera, D., Preibe, N.J. & Glickfeld, L.L. (2022). Feedforward mechanisms of cross-orientation interactions in mouse V1\ • Niell, C.M. & Scanziani M. (2021). How Cortical Circuits Implement Cortical Computations: Mouse Visual Cortex as a Model