New encoding concepts for shape recognition are needed

Ernest Greene

doi:10.3934/Neuroscience.2018.3.162

AIMS Neuroscience, 2018, 5(3): 162-178. doi: 10.3934/Neuroscience.2018.3.162. Review

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New encoding concepts for shape recognition are needed

Ernest Greene

Laboratory for Neurometric Research, Department of Psychology, University of Southern California, Los Angeles, California, USA

Received: , Accepted: , Published:

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Models designed to explain how shapes are perceived and stored by the nervous system commonly emphasize encoding of contour features, especially orientation, curvature, and linear extent. A number of experiments from my laboratory provide evidence that contours deliver a multitude of location markers, and shapes can be identified when relatively few of the markers are displayed. The emphasis on filtering for orientation and other contour features has directed attention away from full and effective examination of how the location information is registered and used for summarizing shapes. Neural network (connectionist) models try to deal with location information by modifying linkage among neuronal populations through training trials. Connections that are initially diffuse and not useful in achieving recognition get eliminated or changed in strength, resulting in selective response to a given shape. But results from my laboratory, reviewed here, demonstrate that unknown shapes that are displayed only once can be identified using a matching task. These findings show that our visual system can immediately encode shape information with no requirement for training trials. This encoding might be accomplished by neuronal circuits in the retina.

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Keywords: shape encoding; orientation filters; connectionist models

Citation: Ernest Greene. New encoding concepts for shape recognition are needed. AIMS Neuroscience, 2018, 5(3): 162-178. doi: 10.3934/Neuroscience.2018.3.162

References:

1. Boorstein DJ (1983) The Discoverers, New York: Random House.
2. Hubel DH, Wiesel TN (1959) Receptive fields of single neurons in the cat's striate cortex. J Physiol 148: 574–591.
3. Hubel DH, Wiesel TN (1968) Receptive fields and functional architecture of monkey striate cortex. J Physiol 195: 215–243.
4. Sceniak MP, Hawken MJ, Shapley R (2001) Visual spatial characterization of macaque V1 neurons. J Neurophysiol 85: 1873–1887.
5. Greene E (2007) Recognition of objects displayed with incomplete sets of discrete boundary dots. Perceptual Mot Skills 104: 1043–1059.
6. Greene E (2007) The integration window for shape cues as a function of ambient illumination. Behav Brain Funct 3: 15.
7. Greene E (2015) Evaluating letter recognition, flicker fusion, and the talbot-plateau law using microsecond-duration flashes. PLoS One 10: e0123458.
8. Greene E (2016) Recognizing words and reading sentences with microsecond flash displays. PLoS One 11: e0145697.
9. Greene E (2008) Additional evidence that contour attributes are not essential cues for object recognition. Behav Brain Funct 4: e26.
10. Greene E (2016) How do we know whether three dots form an equilateral triangle? JSM Brain Sci 1: 1002.
11. 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.
12. Rolls ET (1992) Neurophysiological mechanisms underlying face processing within and beyond the temporal cortical visual areas. Phil Trans R Soc 335: 11–21.
13. Wallis G, Rolls ET (1997) Invariant face and object recognition in the visual system. Prog Neurobiol 51: 167–194.
14. Rodríguezsánchez AJ, Tsotsos JK (2012) The roles of endstopped and curvature tuned computations in a hierarchical representation of 2D shape. PLoS One 7: e42058.
15. Riesenhuber M, Poggio T (2000) Models of object recognition. Nature Neurosci Suppl 3: 1199–1204.
16. Pasupathy A, Connor CE (2001) Shape representation in area V4: Position-specific tuning for boundary conformation. J Neurophysiol 86: 2505–2519.
17. Suzuki N, Hashimoto N, Kashimori Y, et al. (2004) A neural model of predictive recognition in form pathway of visual cortex. Bio Systems 76: 33–42.
18. Pinto N, Cox DD, DeCarlo JJ (2008) Why is real-world visual object recognition hard? PLoS Comput Biol 4: e27.
19. Greene E, Hautus MJ (2017) Demonstrating invariant encoding of shapes using a matching judgment protocol. AIMS Neurosci 4: 120–147.
20. Greene E, Hautus MJ (2018) Evaluating persistence of shape information using a matching protocol. AIMS Neurosci 5:81-96.
21. Greene E, Onwuzulike O (2017) What constitutes elemental shape information for biological vision? Trends Artif Intell 1: 22–26.
22. Greene E (2018) Rapid de novo shape encoding: A challenge to connectionist modeling. arXiv: 1801.02256v1
23. Karplus I, Goren M, Algorn D (1982) A preliminary experimental analysis of predator face recognition by Chromis caenuleus (Pisces, Pomacentridae). Z Tierpsychol 58: 53–65.
24. Siebeck UE, Parker AN, Sprenger D, et al. (2010) A species of reef fish that uses untraviolet pattterns for covert face recognition. Curr Biol 20: 407–410.
25. Karplus I (2006) Predator recognition and social facilitation of predator avoidance in coral reef fish Dascyllus marginatus juveniles. Mar Ecol Prog Ser 319: 215–223.
26. Siebeck UE, Litherland L, Wallis GM (2009) Shape learning and discrimination in reef fish. J Exp Biol 212: 2113–2119.
27. Newport C, Wallis G, Reshitnyk Y, et al. (2016) Discrimination of human faces by archerfish (Toxotes catareus). Sci Rep 6: 27523.
28. Abbas F, Martin MP (2014) Fish vision: Size selectivity in the zebrafish retinotectal pathway. Curr Biol 24: 1048–1050.
29. Greene E, Waksman P (1987) Grid analysis: Continuing the search for a metric of shape. J Math Psychol 31: 338–365.
30. Waksman P, Greene E (1988) Optical image encoding and comparing using scan autocorrelation. United States Patent 4745633.
31. Greene E (2010) Encoding system providing discrimination, classification, and recognition of shapes and patterns. United States Patent 7809195.
32. Greene E (2016) Retinal encoding of shape boundaries. JSM Anat Physiol 1: e1002.
33. Dacey DM (1989) Axon-bearing amacrine cells of the Macaque monkey retina. J Comp Neurol 284: 275–293.
34. Rodieck RW, (1998) The primate retina, In: Steklis HD, Erwin J (eds), Comparative Primate Biology, Vol. 4, Neuroscience, New York, 203–278.
35. Ammermuller J, Weller R (1988) Physiological and morphological characterization of OFF-center amacrine cells in the turtle retina. J Comp Physiol 273: 137–148.
36. Famiglietti EV (1992) Polyaxonal amacrine cells of rabbit retina: Morphology and stratification of PA1 cells. J Comp Neurol 316: 391–405.
37. Famiglietti EV (1992) Polyaxonal amacrine cells of rabbit retina: Size and distribution of PA1 cells. J Comp Neurol 316: 406–421.
38. Freed MA, Pflug R, Kolb H, et al. (1996) ON-OFF amacrine cells in cate retina. J Comp Neural 364: 556–566.
39. Volgi B, Xin D, Amarillo Y, et al. (2001) Morphology and physiology of the polyaxonal amacrine cells in the rabbit retina. J Comp Neurol 440: 109–125.
40. Wright LL, Vaney DI (2004) The type 1 polyaxonal amacrine cells of the rabbit retina: A tracer-coupled study. Visual Neurosci 21: 145–155.
41. Greene E (2007) Retinal encoding of ultrabrief shape recognition cues. PLoS One 2: e871.
42. Nordberg H, Hautus M, Greene E (2018) Visual encoding of partial unknown shape boundaries. AIMS Neurosci 5: 132–147.
43. Bullock TH (1993) Integrative systems research in the brain: Resurgence and new opportunities. Ann Rev Neurosci 16: 1–15.
44. Hopfield JJ (1995) Pattern recognition computation using action potential timing for stimulus representation. Nature 376: 33–36.
45. Thorpe S, Fize D, Marlot C (1996) Speed of processing in the human visual system. Nature 381: 520–522.
46. Thorpe S, Delorme A, VanRullen R (2001) Spike-based strategies for rapid processing. Neural Net 14: 715–725.
47. Vanrullen R, Thorpe SJ (2001) Is it a bird? Is it a plane? Ultra-rapid visual categorisation of natural and artifactual objects. Perception 30: 655–688.
48. Vanrullen R, Thorpe SJ (2002) Surfing a spike wave down the ventral stream. Vision Res 42: 2593–2615.
49. Ahissar E, Arieli A (2012) Seeing via miniature eye movements: A dynamic hypothesis for vision. Front Comput Neurosci 6: e89.
50. Rucci M, Victor JD (2015) The unsteady eye: An information-processing stage, not a bug. Trends Neurosci 38: 195–205.
51. Gollisch T, Meister M (2008) Rapid neural coding in the retina with relative spike latencies. Science 319: 1108–1111.
52. Greene E, Patel Y (2018) Scan encoding of two-dimensional shapes as an alternative neuromorphic concept. Trends Artific Intell 1: 27–33.

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This article has been cited by:

1. Ernest Greene, Jack Morrison, Computational Scaling of Shape Similarity that Has Potential for Neuromorphic Implementation, IEEE Access, 2018, 1, 10.1109/ACCESS.2018.2853656
2. Ernest Greene, Comparing methods for scaling shape similarity, AIMS Neuroscience, 2019, 6, 2, 54, 10.3934/Neuroscience.2019.2.54
3. Taylor Burchfield, Ernest Greene, Michael B. Steinborn, Evaluating spatiotemporal integration of shape cues, PLOS ONE, 2020, 15, 5, e0224530, 10.1371/journal.pone.0224530

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