Abstract
We briefly present some aspects of information processing in the mammalian visual system. The chapter focuses on the problem of scale-independent object recognition. We provide a simple model, based on spiking neurons that make use of shunting inhibition in order to optimally select their driving afferent inputs. The model is able to resist to some degree to scale changes of the stimulus. We discuss possible mechanisms that the brain could use to achieve invariant object recognition and correlate our model with biophysical evidence.
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References
Biederman I (1987) Recognition by components: A theory of human image understanding. Psychol. Rev. 94: 115–147.
Bush P. Sejnowski TJ (1996) Inhibition synchronizes sparsely connected cortical neurons within and between columns in realistic network models. J Comput Neurosci 3: 91–110.
Chrobak JJ, Buzsaki G (1998) Gamma oscillations in the entorhinal cortex of the freely behaving rat. J Neurosci 18: 388–398.
Delorme A, Thorpe SJ (2001) Face identification using one spike per neuron: resistance to image degradations. Neural Networks 14: 795–803.
Freeman WJ (1975) Mass Action in the Nervous System. Academic Press, NewYork.
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.
Gray CM (1999) The Temporal Correlation Hypothesis of Visual Feature Integration: Still Alive and Well. Neuron 24: 31–47.
Hubel D, Wiesel T (1965) Receptive fields and functional architecture in two nonstriate visual areas (18 and 19) of the cat. J Neurophysiol 28: 229–289.
Li Z (1998) A neural model of contour integration in the primary visual cortex. Neural Comput 10 (4): 903–40.
Li Z (1999) Visual segmentation by contextual influences via intra-cortical interactions in the primary visual cortex. Network: Comput Neural Syst 10: 187–212.
Lytton WW, Sejnowski TJ (1991) Simulations of cortical pyramidal neurons synchronized by inhibitory interneurons. J Neurophysiol 66: 1059–1079.
Maass W, Legenstein RA, Markram H (2002) A new approach towards vision suggested by biologically realistic neural microcircuit models. In Buelthof 11H, Lee SW, Poggio TA, Wallraven C (eds) Biologically Motivated Computer Vision. Proc of the Second International Workshop BMCV 2002 Tübingen, Germany, vol. 2525 of Lecture Notes in Computer Science, Springer, Berlin, pp 282–293.
von der Malsburg C (1981) The correlation theory of brain function. MPI Biophysical Chemistry Internal Report 81–2.
von der Malsburg C (1985) Nervous structures with dynamical links Ber Bunsenges Phys Chem 89: 703–710.
von der Malsburg C (1986) Am I thinking assemblies? In: Palm G, Aertsen A (eds) Proceedings of the Trieste Meeting on Brain Theory. Springer, Berlin.
von der Malsburg C (1999) The What and Why of Binding: The Modeler’s Perspective. Neuron 24: 95–104.
Milner P (1974) A model for visual shape recognition. Psychol Rev 81: 521–535.
Muresan RC (2002) Complex Object Recognition Using a Biologically Plausible Neural Model. In: Mastorakis NE (eds) Advances in Simulation, Systems Theory and Systems Engineering. WSEAS Press, Athens, pp 163–168.
Muresan RC (2002) Visual Scale Independence in a Network of Spiking Neurons. ICONIP ‘02 Proceedings, Singapore, 4: 1739–1743.
Muresan RC (2003) RetinotopicNET: An Efficient Simulator for Retinotopic Visual Architectures. ESANN’03 Proceedings, Bruges, pp 247–254.
Muresan RC (2003) Pattern Recognition Using Pulse-Coupled Neural Networks and Discrete Fourier Transforms. Neurocomputing 51C: 487–493.
Muresan RC (2003) The Coherence Theory: Simple Attentional Modulation Effects. CNS’03 Proceeding, in press.
Olshausen BA, Anderson CH, van Essen DC (1993) A Neurobiological Model of Visual Attention and Invariant Pattern Recognition Based on Dynamic Routing of Information. J Neurosci 13 (11): 4700–4719.
Pasupathy Anitha, Connor CE (1999) Responses to Contour Features in Macaque Area V4. J Neurophysiol 82: 2490–2502.
Riesenhuber M, Poggio T (1999) Hierarchical models of object recognition in cortex. Nat Neurosci 2 (11): 1019–1025.
Shadlen MN, Newsome WT (1994) Noise, neural codes and cortical organization. Curr Opin Neurobiol 4: 569–579.
Shadlen MN, Newsome WT (1998) The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J Neurosci 18: 3870–3896.
Thorpe SJ, Fize D, Marlot C (1996) Speed of processing in the human visual system. Nature 381 (6582): 520–522.
Thorpe SJ, Gaufrais J (1998) Rank order coding. In: Bower J (eds) Computational neuroscience: Trends in research. Plenum Press, New York, pp 113–118.
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Muresan, R.C. (2004). Scale Independence in the Visual System. In: Rajapakse, J.C., Wang, L. (eds) Neural Information Processing: Research and Development. Studies in Fuzziness and Soft Computing, vol 152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39935-3_1
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DOI: https://doi.org/10.1007/978-3-540-39935-3_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-53564-2
Online ISBN: 978-3-540-39935-3
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