Skip to main content Accessibility help
×
Hostname: page-component-848d4c4894-tn8tq Total loading time: 0 Render date: 2024-06-20T18:35:14.334Z Has data issue: false hasContentIssue false

3 - Correlation versus gradient type motion detectors: the pros and cons

from Part II - The use of artificial neural networks to elucidate the nature of perceptual processes in animals

Published online by Cambridge University Press:  05 July 2011

Alexander Borst
Affiliation:
Max Planck Institute for Neurobiology, Systems and Computational Neurobiology
Colin R. Tosh
Affiliation:
University of Leeds
Graeme D. Ruxton
Affiliation:
University of Glasgow
Get access

Summary

Introduction

In motion vision, two distinct models have been proposed to account for direction-selectivity: the Reichardt detector and the gradient detector (Figure 3.1). In the Reichardt detector (also called ‘Hassenstein–Reichardt’ detector or correlation-type motion detector), the luminance levels of two neighbouring image locations are multiplied after being filtered asymmetrically (Figure 3.1, left). This operation is performed twice in a mirror-symmetrical fashion, before the outputs of both multipliers are subtracted from each other (Hassenstein & Reichardt,1956; Reichardt, 1961, 1987; Borst & Egelhaaf, 1989). The spatial or temporal average of such local motion detector signals is proportional to the image velocity within a range set by the detector time-constant (Egelhaaf & Reichardt, 1987). However, it is one of the hallmarks of this model that the output of the individual velocity detectors depends, in addition to stimulus velocity, in a characteristic way on the spatial structure of the moving pattern: in response to drifting gratings, for example, the local Reichardt detector output consists of two components: a sustained (DC) component which indicates by its sign the direction of the moving stimulus, and an AC component, which follows the local intensity modulation and, thus, carries no directional information at all. Since the local intensity modulations are phase-shifted with respect to each other, the AC components in the local signals become cancelled by spatial integration of many adjacent detectors. Unlike the AC component, the DC component survives spatial or temporal averaging (integration). The global output signal, therefore, is purely directional.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adelson, E. H. & Bergen, J. R. 1985. Spatiotemporal energy models for the perception of motion. J Opt Soc Am A 2, 284–299.CrossRefGoogle ScholarPubMed
Borst, A. & Egelhaaf, M. 1989. Principles of visual motion detection. Trends Neurosci 12, 297–306.CrossRefGoogle ScholarPubMed
Borst, A. & Egelhaaf, M. 1993. Detecting visual motion: theory and models. In Visual Motion and its Role in the Stabilization of Gaze (ed. F. A. & Wallman, J.), pp. 3–27. Elsevier.Google Scholar
Borst, A. & Haag, J. 2002. Neural networks in the cockpit of the fly. J Comp Physiol 188, 419–437.Google Scholar
Borst, A. 2003. Noise, not stimulus entropy, determines neural information rate. J Computat Neurosci 14, 23–31.CrossRefGoogle Scholar
Borst, A., Flanagin, V. & Sompolinsky, H. 2005. Adaptation without parameter change: dynamic gain control in motion detection. PNAS 102, 6172–6176.CrossRefGoogle ScholarPubMed
Brenner, N., Bialek, W. & Ruyter, R. R. 2000. Adaptive rescaling maximizes information transmission. Neuron 26, 695–702.CrossRefGoogle ScholarPubMed
Buchner, E. 1976. Elementary movement detectors in an insect visual system. Biol Cybern. 24, 85–10.CrossRefGoogle Scholar
Ruyter van Steveninck, R. R., Lewen, G. D., Strong, S. P.et al. 1997. Reproducibility and variability in neural spike trains. Science 275, 1805–1808.CrossRefGoogle ScholarPubMed
Dickinson, M. H., Lehmann, F.-O. & Sane, S. P. 1999. Wing rotation and the aerodynamic basis of insect flight. Science 284, 1954–1960.CrossRefGoogle ScholarPubMed
Egelhaaf, M. & Reichardt, W. 1987. Dynamic response properties of movement detectors: theoretical analysis and electrophysiological investigation in the visual system of the fly. Biol Cybern 56, 69–87.CrossRefGoogle Scholar
Fairhall, A. L., Lewen, G. D., Bialek, W. & Ruyter, R. R. 2001. Efficiency and ambiguity in an adaptive neural code. Nature 412, 787–792.CrossRefGoogle Scholar
Fermi, G. & Reichardt, W. 1963. Optomotorische Reaktionen der Fliege Musca domestica. Abhängigkeit der Reaktion von der Wellenlänge, der Geschwindigkeit, dem Kontrast und der mittleren Leuchtdichte bewegter periodischer Muster. Kybernetik 2, 15–28.CrossRefGoogle Scholar
Fennema, C. L. & Thompson, W. B. 1979. Velocity determination in scenes containing several moving objects. Comp Graph Im Process 9, 301–315.CrossRefGoogle Scholar
Götz, K. G. 1964. Optomotorische Untersuchungen des visuellen Systems einiger Augenmutanten der Fruchtfliege Drosophila. Kybernetik 2, 77–92.CrossRefGoogle Scholar
Hassenstein, B. & Reichardt, W. 1956. Systemtheoretische Analyse der Zeit-Reihenfolgen- und Vorzeichenauswertung bei der Bewegungsperzeption des Rüsselkäfers Chlorophanus. Z Naturforsch 11b, 513–524.CrossRefGoogle Scholar
Haag, J., Denk, W. & Borst, A. 2004. Fly motion vision is based on Reichardt detectors regardless of the signal-to-noise ratio. PNAS 101, 16333–16338.CrossRefGoogle ScholarPubMed
Hildreth, E. & Koch, C. 1987. The analysis of motion: from computational theory to neural mechanisms. Ann Rev Neurosci 10, 477–533.CrossRefGoogle Scholar
Limb, J. O. & Murphy, J. A. 1975. Estimating the velocity of moving images in television signals. CompGraph Im Process 4, 311–327.Google Scholar
Lei, S. & Borst, A. 2006. Propagation of photon noise and information transfer in visual motion detection. J Computat Neurosci 20, 167–178.Google Scholar
Potters, M. & Bialek, W. 1994. Statistical mechanics and visual signal processing. J PhysiolFrance 4, 1755–1775.Google Scholar
Reichardt, W. 1961. Autocorrelation, a principle for the evaluation of sensory information by the central nervous system. In Sensory Communication, (ed. Rosenblith, W. A.), pp. 303–317. MIT Press and John Wiley & Sons.Google Scholar
Reichardt, W. 1987. Evaluation of optical motion information by movement detectors. J Comp PhysiolA 161, 533–547.CrossRefGoogle ScholarPubMed
Srinivasan, M. V. 1990. Generalized gradient schemes for measurement of image motion. Biol Cybern 63, 421–443.CrossRefGoogle ScholarPubMed
Single, S. & Borst, A. 1998. Dendritic integration and its role in computing image velocity. Science 281, 1848–1850.CrossRefGoogle ScholarPubMed
Santen, J. P. H. & Sperling, G. 1985. Elaborated Reichardt detectors. J Opt Soc Am A 2, 300–320.CrossRefGoogle ScholarPubMed
Hateren, J. H., Kern, R., Schwerdtfeger, G. & Egelhaaf, M. 2005. Function and coding in the blowfly H1 neuron during naturalistic optic flow. J Neurosci 25, 4343–4352.CrossRefGoogle ScholarPubMed

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×