Reading List

Core Readings

1. Medathati, Neumann, Masson & Kornprobst (2016). Bio-inspired computer vision: towards a synergistic approach of artificial and biological vision. CVIU 150.
2. Marr (1982). Vision, ch. 1–3. Freeman (MIT Press reissue 2010).
3. Olshausen & Field (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381.
4. Carandini & Heeger (2012). Normalization as a canonical neural computation. Nature Reviews Neuroscience 13.
5. Rao & Ballard (1999). Predictive coding in the visual cortex. Nature Neuroscience 2.
6. Adelson & Bergen (1985). Spatiotemporal energy models for the perception of motion. JOSA A 2.
7. Ohzawa, DeAngelis & Freeman (1990). Stereoscopic depth discrimination in the visual cortex: neurons ideally suited as disparity detectors. Science 249.
8. Marr & Poggio (1979). A computational theory of human stereo vision. Proc. R. Soc. B 204.
9. Ballard (1991). Animate vision. Artificial Intelligence 48.
10. Traver & Bernardino (2010). A review of log-polar imaging for visual perception in robotics. Robotics and Autonomous Systems 58.
11. Yamins et al. (2014). Performance-optimized hierarchical models predict neural responses in higher visual cortex. PNAS 111.
12. Gallego et al. (2022). Event-based vision: a survey. IEEE TPAMI 44.

Textbooks

• Zhaoping (2014). Understanding Vision: Theory, Models, and Data. Oxford University Press.
• Dayan & Abbott (2001). Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press.
• Mallot (2000). Computational Vision: Information Processing in Perception and Visual Behavior. MIT Press.
• Marr (1982). Vision. Freeman (MIT Press reissue 2010).
• Hyvärinen, Hurri & Hoyer (2009). Natural Image Statistics. Springer.
• Frisby & Stone (2010). Seeing: The Computational Approach to Biological Vision, 2nd ed. MIT Press.
• Howard & Rogers (2012). Perceiving in Depth, 3 vols. Oxford University Press.
• Cristóbal, Perrinet & Keil, eds. (2015). Biologically Inspired Computer Vision. Wiley-VCH.
• Poggio & Anselmi (2016). Visual Cortex and Deep Networks. MIT Press.
• Liu, Delbruck, Indiveri, Whatley & Douglas (2015). Event-Based Neuromorphic Systems. Wiley.

Retina, Sampling and Foveation

• Curcio, Sloan, Kalina & Hendrickson (1990). Human photoreceptor topography. Journal of Comparative Neurology 292.
• Atick & Redlich (1992). What does the retina know about natural scenes? Neural Computation 4.
• Wässle (2004). Parallel processing in the mammalian retina. Nature Reviews Neuroscience 5.
• Gollisch & Meister (2010). Eye smarter than scientists believed: neural computations in circuits of the retina. Neuron 65.
• Schwartz (1977). Spatial mapping in the primate sensory projection: analytic structure and relevance to perception. Biological Cybernetics 25.
• Bolduc & Levine (1998). A review of biologically motivated space-variant data reduction models for robotic vision. CVIU 69.
• Traver & Bernardino (2010). A review of log-polar imaging for visual perception in robotics. Robotics and Autonomous Systems 58.
• Rosenholtz (2016). Capabilities and limitations of peripheral vision. Annual Review of Vision Science 2.
• Freeman & Simoncelli (2011). Metamers of the ventral stream. Nature Neuroscience 14.
• Guenter, Finch, Drucker, Tan & Snyder (2012). Foveated 3D graphics. ACM TOG 31(6).
• Patney et al. (2016). Towards foveated rendering for gaze-tracked virtual reality. ACM TOG 35(6).

Early Cortical Models

• Hubel & Wiesel (1962). Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. Journal of Physiology 160.
• Daugman (1985). Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. JOSA A 2.
• Koenderink (1984). The structure of images. Biological Cybernetics 50.
• Freeman & Adelson (1991). The design and use of steerable filters. IEEE TPAMI 13.
• Poggio, Torre & Koch (1985). Computational vision and regularization theory. Nature 317.
• Olshausen & Field (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381.
• Simoncelli & Olshausen (2001). Natural image statistics and neural representation. Annual Review of Neuroscience 24.
• Heeger (1992). Normalization of cell responses in cat striate cortex. Visual Neuroscience 9.
• Carandini & Heeger (2012). Normalization as a canonical neural computation. Nature Reviews Neuroscience 13.
• Rao & Ballard (1999). Predictive coding in the visual cortex. Nature Neuroscience 2.
• Wilson & Cowan (1973). A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue. Kybernetik 13.
• Amari (1977). Dynamics of pattern formation in lateral-inhibition type neural fields. Biological Cybernetics 27.
• Ben-Yishai, Bar-Or & Sompolinsky (1995). Theory of orientation tuning in visual cortex. PNAS 92.
• Olshausen & Field (2005). How close are we to understanding V1? Neural Computation 17.

Motion

• Hassenstein & Reichardt (1956). Systemtheoretische Analyse der Zeit-, Reihenfolgen- und Vorzeichenauswertung bei der Bewegungsperzeption des Rüsselkäfers Chlorophanus. Z. Naturforsch. B 11.
• Adelson & Bergen (1985). Spatiotemporal energy models for the perception of motion. JOSA A 2.
• van Santen & Sperling (1985). Elaborated Reichardt detectors. JOSA A 2.
• Heeger (1988). Optical flow using spatiotemporal filters. IJCV 1.
• Simoncelli & Heeger (1998). A model of neuronal responses in visual area MT. Vision Research 38.
• Weiss, Simoncelli & Adelson (2002). Motion illusions as optimal percepts. Nature Neuroscience 5.
• Borst & Euler (2011). Seeing things in motion: models, circuits, and mechanisms. Neuron 71.

Stereo and Binocular Vision

• Ohzawa, DeAngelis & Freeman (1990). Stereoscopic depth discrimination in the visual cortex: neurons ideally suited as disparity detectors. Science 249.
• Qian (1994). Computing stereo disparity and motion with known binocular cell properties. Neural Computation 6.
• Fleet, Wagner & Heeger (1996). Neural encoding of binocular disparity: energy models, position shifts and phase shifts. Vision Research 36.
• Cumming & DeAngelis (2001). The physiology of stereopsis. Annual Review of Neuroscience 24.
• Marr & Poggio (1976). Cooperative computation of stereo disparity. Science 194.
• Marr & Poggio (1979). A computational theory of human stereo vision. Proc. R. Soc. B 204.
• Pollard, Mayhew & Frisby (1985). PMF: a stereo correspondence algorithm using a disparity gradient limit. Perception 14.
• Scharstein & Szeliski (2002). A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV 47.
• Nakayama & Shimojo (1990). Da Vinci stereopsis: depth and subjective occluding contours from unpaired image points. Vision Research 30.
• Belhumeur & Mumford (1992). A Bayesian treatment of the stereo correspondence problem using half-occluded regions. CVPR.
• Anderson & Nakayama (1994). Toward a general theory of stereopsis: binocular matching, occluding contours, and fusion. Psychological Review 101.
• Kanade & Okutomi (1994). A stereo matching algorithm with an adaptive window. IEEE TPAMI 16.
• Yoon & Kweon (2006). Adaptive support-weight approach for correspondence search. IEEE TPAMI 28.
• Lipson, Teed & Deng (2021). RAFT-Stereo: multilevel recurrent field transforms for stereo matching. 3DV.
• Xu, Wang, Yang & Zhang (2023). Iterative geometry encoding volume for stereo matching (IGEV-Stereo). CVPR.
• Wen et al. (2025). FoundationStereo: zero-shot stereo matching. CVPR.

Eye Movements, Attention and Active Vision

• Bajcsy (1988). Active perception. Proceedings of the IEEE 76.
• Aloimonos, Weiss & Bandyopadhyay (1988). Active vision. IJCV 1.
• Ballard (1991). Animate vision. Artificial Intelligence 48.
• Yarbus (1967). Eye Movements and Vision. Plenum.
• Land & Hayhoe (2001). In what ways do eye movements contribute to everyday activities? Vision Research 41.
• Itti, Koch & Niebur (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE TPAMI 20.
• Najemnik & Geisler (2005). Optimal eye movement strategies in visual search. Nature 434.
• Mnih, Heess, Graves & Kavukcuoglu (2014). Recurrent models of visual attention. NeurIPS.

Recognition and the Ventral Stream

• Fukushima (1980). Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36.
• Riesenhuber & Poggio (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience 2.
• Serre, Oliva & Poggio (2007). A feedforward architecture accounts for rapid categorization. PNAS 104.
• DiCarlo, Zoccolan & Rust (2012). How does the brain solve visual object recognition? Neuron 73.
• Yamins et al. (2014). Performance-optimized hierarchical models predict neural responses in higher visual cortex. PNAS 111.
• Khaligh-Razavi & Kriegeskorte (2014). Deep supervised, but not unsupervised, models may explain IT cortical representation. PLoS Computational Biology 10.
• Kriegeskorte (2015). Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual Review of Vision Science 1.
• Schrimpf et al. (2020). Integrative benchmarking to advance neurally mechanistic models of human intelligence. Neuron 108.
• Geirhos et al. (2019). ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. ICLR.
• Dapello et al. (2020). Simulating a primary visual cortex at the front of CNNs improves robustness to image perturbations. NeurIPS.
• Lindsay (2021). Convolutional neural networks as a model of the visual system: past, present, and future. Journal of Cognitive Neuroscience 33.

Neuromorphic Sensing and Event-Based Vision

• Mead (1990). Neuromorphic electronic systems. Proceedings of the IEEE 78.
• Mahowald & Mead (1991). The silicon retina. Scientific American 264.
• Indiveri & Douglas (2000). Neuromorphic vision sensors. Science 288.
• Lichtsteiner, Posch & Delbruck (2008). A 128×128 120 dB 15 µs latency asynchronous temporal contrast vision sensor. IEEE JSSC 43.
• Gallego et al. (2022). Event-based vision: a survey. IEEE TPAMI 44.
• Bartolozzi, Indiveri & Donati (2022). Embodied neuromorphic intelligence. Nature Communications 13.
• D'Angelo et al. (2022). Event driven bio-inspired attentive system for the iCub humanoid robot on SpiNNaker. Neuromorphic Computing and Engineering 2.
• Osswald, Ieng, Benosman & Indiveri (2017). A spiking neural network model of 3D perception for event-based neuromorphic stereo vision systems. Scientific Reports 7.
• Maass (1997). Networks of spiking neurons: the third generation of neural network models. Neural Networks 10.
• Neftci, Mostafa & Zenke (2019). Surrogate gradient learning in spiking neural networks. IEEE Signal Processing Magazine 36.
• Davies et al. (2018). Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro 38.

Courses and Other Resources

• Neuromorphic Computing (University of Genoa, S. P. Sabatini)
• Neuromorphic Algorithms for Modeling Visual Perception (CTU Prague)
• CTU Event-Driven Neuromorphic Labs (lab notebooks)
• Brains, Minds and Machines Summer Course (lectures and tutorials)
• Systems Vision Science Summer School
• Telluride Neuromorphic AI Workshop

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