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A Unified Quantitative Model of Vision and Audition

Liu, Mr. Peilei and Wang, Professor Ting (2014) A Unified Quantitative Model of Vision and Audition. [Preprint]

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Abstract

We have put forwards a unified quantitative framework of vision and audition, based on existing data and theories. According to this model, the retina is a feedforward network self-adaptive to inputs in a specific period. After fully grown, cells become specialized detectors based on statistics of stimulus history. This model has provided explanations for perception mechanisms of colour, shape, depth and motion. Moreover, based on this ground we have put forwards a bold conjecture that single ear can detect sound’s direction. This is complementary to existing theories and has provided better explanations for sound localization.

Item Type:Preprint
Keywords:perception mechanisms, encoding model, vision, sound location
Subjects:Neuroscience > Computational Neuroscience
Computer Science > Machine Vision
Computer Science > Statistical Models
Neuroscience > Neural Modelling
ID Code:9751
Deposited By: Liu, Mr. Peilei
Deposited On:24 Aug 2014 21:06
Last Modified:20 Apr 2015 11:40

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

1. Crick, F. The Astonishing Hypothesis: The Scientific Search for the Soul (Charles Scribner's Sons, New York, 1994).

2. Livingstone, M., Hubel, & D. Segregation of Form, Color, Movement, and Depth: Anatomy, Physiology, and Perception. Science 240, 740-749 (1988).

3. Marr, D. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information (Freeman, New York, 1982).

4. Marr, D. & Poggio, T. Cooperative computation of stereo disparity. Science 194, 283-287 (1976).

5. Joris, P.X., Smith, P.H. & Yin, T.C.T. Coincidence Detection Minireview in the Auditory System: 50 Years after Jeffress. Neuron 21, 1235-1238 (1998).

6. Ashida, G. & Carr, C.E. Sound localization: Jeffress and beyond. Curr. Opin. Neurobiol. 21, 745-751 (2011).

7. Koch, C. & Hepp, K. Quantum mechanics in the brain. Nature 440, 611-612 (2006).

8. Poggio, T. & Bizzi, E. Generalization in vision and motor control. Nature 43, 768-774 (2004).

9. Solomon, S.G. & Lennie, P. The machinery of colour vision. Nature Rev. Neurosci. 8, 276-286 (2007).

10. Borst, A. & Euler, T. Seeing Things in Motion: Models, Circuits, and Mechanisms. Neuron 71, 974-994 (2011).

11. Tsutsuia, K., Tairab, M. & Sakata, H. Neural mechanisms of three-dimensional vision. Neurosci. Res. 51, 221-229 (2005).

12. McCulloch, W.S. & Pitts, W. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biol. 5, 115-133 (1943).

13. Hodgkin, A.L. & Huxley, A.F. A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117, 500-544 (1952).

14. Grossberg, S. Adaptive resonance theory: how a brain learns to consciously attend, learn, and recognize a changing world. Neural Networks 37, 1-47 (2013).

15. Hebb, D.O. The Organisation of Behavior: A Neuropsychological Approach (Jhon & Sons, New York, 1949).

16. Bienenstock, E.L., Cooper, L.N. & Munro, P.W. Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex. J. Neurosci. 2, 28-32 (1982).

17. Wiesen, T.N. & Hubel, D.H. Effects of visual deprivation on morphology and physiology of cell in the cat's lateral geniculate body. J. Physiol. 26, 978-993(1963).

18. Antonini, A. & Stryker, M.P. Rapid Remodeling of Axonal Arbors in the Visual Cortex. Science 260, 19-21(1993).

19. Neumann, J. V. The Computer and the Brain (Yale University Press, New Haven, CT, 2000).

20. Schrödinger, E. What Is Life? (Cambridge University Press, Cambridge, 1944).

21. Ackley, D.H., Hinton, G.E. & Sejnowski, T.J. A learning algorithm for Boltzmann machine. Cognit. Sci. 9, 147-169 (1985).

22. Hubel, D.H. & Wiesel, T.N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J. Physiol. 160, 106-154 (1962).

23. Hinton, G.E. & Salakhutdinov, R.R. Reducing the dimensionality of data with neural networks. Science 313, 504-507 (2006).

24. Gross, C.G. Genealogy of the "Grandmother Cell". Neuroscientist 8, 512-518 (2002).

25. Geiger, G. & Poggio, T. The Muller-Lyer figure and the fly. Science 190, 479-480 (1975).

26. Robert, C. thesis, Monaural and binaural level cues: a behavioral and physiological investigation. University of Oxford (2006).

27. Shinn-Cunningham, B.S., Santarelli, S. & Kopco, N. Tori of confusion: binaural localization cues for sources within reach of a listener. J. Acoust. Soc. Am. 107, 1627-1636 (2000).

28. Simmons, J. A. Bats use a neuronally implemented computational acoustic model to form sonar images. Curr. Opin. Neurobiol. 22, 311-319 (2012).

29. Van Wanrooij, M.M. & Van Opstal, A.J. Contribution of head shadow and pinna cues to chronic monaural sound localization. J. Neurosci. 24, 4163-4171 (2004).

30. Goldstein, B. Sensation and Perception, 6th ed. (Wadsworth, London, 2001).

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