Definition
Hierarchical models of the visual system are neural networks with a layered topology: In these networks, the receptive fields (i.e., the region of the visual space that units respond to) of units at one level of the hierarchy are constructed by combining inputs from units at a lower level. After a few processing stages, small receptive fields tuned to simple stimuli get combined to form larger receptive fields tuned to more complex stimuli. Such anatomical and functional hierarchical architecture is a hallmark of the organization of the visual system.
Since the pioneering work of Hubel and Wiesel (1962), a variety of hierarchical models have been described from relatively small-scale models of the primary visual cortex to very large-scale (system-level) models of object and action recognition, which account for processing in large portions of the visual field...
This is a preview of subscription content, log in via an institution.
References
Amit Y, Mascaro M (2003) An integrated network for invariant visual detection and recognition. Vision Res 43(19):2073–2088
Biederman I (1987) Recognition-by-components: a theory of human image understanding. Psychol Rev 94(2):115–147
Carandini M, Heeger DJ (2011) Normalization as a canonical neural computation. Nature Rev Neurosci 13:51–62
Chance FS, Nelson SB, Abbott LF (2000) A recurrent network model for the phase invariance of complex cell responses. Neurocomputing 32:339–344
Chen X, Han F, Poo M-m, Dan Y (2007) Excitatory and suppressive receptive field subunits in awake monkey primary visual cortex (V1). Proc Natl Acad Sci U S A 104(48):19120–19125
Dayan P, Abbott LF (2001) Theoretical neuroscience: computational and mathematical modeling of neural systems. MIT Press, Cambridge, MA
Desimone R, Albright TD, Gross CG, Bruce C (1984) Stimulus-selective properties of inferior temporal neurons in the macaque. J Neurosci 4(8):2051–2062
Dicarlo JJ, Zoccolan D, Rust NC (2012) How does the brain solve visual object recognition? Neuron 73(3):415–434
Felleman DJ, Van Essen DC (1991) Distributed hierarchical processing in the primate cerebral cortex. Cereb Cortex (New York, NY: 1991) 1(1):1–47
Fukushima K (1980) Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 36:193–202
Geman S (1999) Hierarchy in machine and natural vision. In: Proceedings of the 11th Scandinavian Conference on Image Analysis, Kangerlusssuaq, Greenland, pp 7–11
Giese MA, Poggio T (2003) Neural mechanisms for the recognition of biological movements. Neuroscience 4:179–192
Grossberg S, Mingolla E, Pack C (1999) A neural model of motion processing and visual navigation by cortical area MST. Cereb Cortex 9(8):878–895
Grossberg S, Markowitz J, Cao Y (2011a) On the road to invariant recognition: explaining tradeoff and morph properties of cells in inferotemporal cortex using multiple-scale task-sensitive attentive learning. Neural Netw 24(10):1036–1049
Grossberg S, Srinivasan K, Yazdanbakhsh A (2011b) On the road to invariant object recognition: how cortical area V2 transforms absolute to relative disparity during 3D vision. Neural Netw 24(7):686–692
Hegdé J, Essen DV (2007) A comparative study of shape representation in macaque visual areas V2 and V4. Cereb Cortex 17:1100–1116
Hegdé J, Felleman DJ (2007) Reappraising the functional implications of the primate visual anatomical hierarchy. Neuroscientist 13(5):416–421
Hochstein S, Ahissar M (2002) View from the top: hierarchies and reverse hierarchies in the visual system. Neuron 36(5):791–804
Hubel D, Wiesel T (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol 160:106–154
Jhuang H, Serre T, Wolf L, Poggio T (2007) A biologically inspired system for action recognition. In: 2007 I.E. 11th international conference on computer vision, Rio de Janeiro, Brazil pp 14–20
Jones JP, Palmer LA (1987) An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. J Neurophysiol 58(6):1233–1258
Kouh M, Poggio T (2008) A general mechanism for cortical tuning: normalization and synapses can create Gaussian-like tuning. 20(6):1427–1451
Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Infr Proc Syst 2:1097–1105
Landy M, Movshon J (1991) Computational models of visual processing. Bradford Books Cambridge, MA
LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324
Marko H, Giebel H (1970) Recognition of handwritten characters with a system of homogeneous layers. Nachrichtentechnische Zeitschrift 23:455–459
Maruyama M, Girosi F, Poggio T (1992) A connection between GRBF and MLP. MIT, Cambridge, MA
Masquelier T, Thorpe SJ (2007) Unsupervised learning of visual features through spike timing dependent plasticity. PLoS Comput Biol 3(2):e31
Mel BW (1997) SEEMORE: combining color, shape, and texture histogramming in a neurally inspired approach to visual object recognition. Neural Comput 9:777–804
Mineault P, Khawaja F, Butts D, Pack C (2012) Hierarchical processing of complex motion along the primate dorsal visual pathway. Proc Natl Acad Sci U S A 109(16):E972–E980
O’Reilly RC, Wyatte D, Herd S, Mingus B, Jilk DJ (2013) Recurrent processing during object recognition. Front Psychol 4:1–14
Ostojic S, Brunel N (2011) From spiking neuron models to linear-nonlinear models. PLoS Comput Biol 7(1):e1001056
Pack CC, Born RT (2008) Cortical mechanisms for the integration of visual motion. Elsevier, Oxford
Perrett D, Oram M (1993) Neurophysiology of shape processing. Image Vis Comput 11(6):317–333
Perrone J, Thiele A (2002) A model of speed tuning in MT neurons. Vis Res 42(8):1035–1051
Poggio T, Smale S (2003) The Mathematics of Learning: Dealing with Data. Notices Amer. Math. Soc 50(5):537–544
Rieke F, Warland D, van Steveninck R, Bialek W, van Steveninck R (1997) Spikes. MIT Press, Cambridge, MA
Riesenhuber M, Poggio T (1999) Hierarchical models of object recognition in cortex. Nat Neurosci 2:1019–1025
Ringach DL (2004) Mapping receptive fields in primary visual cortex. J Physiol 558(Pt 3):717–728
Rust NC, Schwartz O, Movshon JA, Simoncelli EP (2005) Spatiotemporal elements of macaque v1 receptive fields. Neuron 46(6):945–956
Rust NC, Mante V, Simoncelli EP, Movshon JA (2006) How MT cells analyze the motion of visual patterns. Nat Neurosci 9(11):1421–1431
Series P, Lorenceau J, Fregnac Y (2003) The silent surround of V1 receptive fields: theory and experiments. J Physiol 97:453–474
Serre T, Poggio T (2010) A neuromorphic approach to computer vision. Commun ACM 53(10):54
Serre T, Kreiman G, Kouh M, Cadieu C, Knoblich U, Poggio T (2007a) A quantitative theory of immediate visual recognition. Prog Brain Res 165:33
Serre T, Kreiman G, Kouh M, Cadieu C, Knoblich U, Poggio T (2007b) A quantitative theory of immediate visual recognition. Prog Brain Res 165(06):33–56
Simoncelli EP, Heeger DJ (1998) A model of neuronal responses in visual area MT. Vision Res 38(5):743–761
Thorpe SJ (2002) Ultra-rapid scene categorisation with a wave of spikes. Proc Biol Mot Comput Vis, 25(25):1–15
Tsao DY, Moeller S, Freiwald WA (2008) Comparing face patch systems in macaques and humans. Proc Natl Acad Sci U S A 105(49):19514–19519
Ullman S, Vidal-Naquet M, Sali E (2002) Visual features of intermediate complexity and their use in classification. Nat Neurosci 5(7):682–687
Wallis G, Rolls ET (1997) A model of invariant object recognition in the visual system. Prog Neurobiol 51:167–194
Wersing H, Koerner E (2003) Learning optimized features for hierarchical models of invariant recognition. Neural Comput 15(7):1559–1588
Further Reading
Kreiman G (2008) Biological object recognition. Scholarpedia 3(6):2667
Poggio T, Serre T (2013) Models of visual cortex. Scholarpedia 8(4):3516
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this entry
Cite this entry
Serre, T. (2014). Hierarchical Models of the Visual System. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_345-1
Download citation
DOI: https://doi.org/10.1007/978-1-4614-7320-6_345-1
Received:
Accepted:
Published:
Publisher Name: Springer, New York, NY
Online ISBN: 978-1-4614-7320-6
eBook Packages: Springer Reference Biomedicine and Life SciencesReference Module Biomedical and Life Sciences
Publish with us
Chapter history
-
Latest
Hierarchical Models of the Visual System- Published:
- 12 March 2020
DOI: https://doi.org/10.1007/978-1-4614-7320-6_345-2
-
Original
Hierarchical Models of the Visual System- Published:
- 26 March 2014
DOI: https://doi.org/10.1007/978-1-4614-7320-6_345-1