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

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

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

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

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

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

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

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

Suggested Readings

Emily Cooper

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

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

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

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

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

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

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