Abstract
Up to the cortical simple cells there exist some good linear models of the visual system neurons’ dynamic receptive fields. These linear approximations are based on considerable amount of experimental evidences. Based on the linear approach, assumptions can be made about noise filtering characteristics of the early visual processing. Unfortunately, these notions, based primarily on power spectral estimations, can not be used when neurons’ responses reach the nonlinear part of their dynamic range. Real neurons’ response, however, can frequently reach this region. We made simple Cellular Neural Network (CNN) models to simulate the retinal ganglion cells dynamic receptive fields. Using an apt inversion method, in the root mean square error sense optimal reconstruction can be achieved even from the modeled nonlinearly mapped responses. This way we can predict the nonlinear system’s noise filtering properties. Using the CNN as a modeling frame it is easy to implement both the deconvolution and the necessary additional processing steps as well to establish reconstruction. By this technique, depending on the properties of the additive and intrinsic noise terms we could estimate, from the noise filtering point of view, ideal parameters of the dynamic receptive fields’ linear and even simple nonlinear functions. Our results can explain the measured effects of dark adaptation on the receptive field structure and can give some insight to the design of the probable further information processing steps. This type of preprocessing can ameliorate the efficiency some of the existing image compression algorithms, and using CNN technology, the necessary reconstruction can be even accomplished in real time.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Atick J.J., Redlich A.N. (1992) What does the retina know about Natural Scenes? Neural computation Vol. 4. pp. 196–210
Chua L.O. and Roska T. (1993) The CNN paradigm. IEEE Trans. CAS-I, Vol. 40, pp. 147–156
Chua L.O. and Yang L. (1988) Cellular Neural Networks: Theory. IEEE Trans. on Circuits and Systems, (CAS), Vol. 35. pp. 1257–1272
Daqing Cai, DeAngelis G.C. and Freeman R.D. (1997) Spatiotemporal Receptive Field Organization in the Lateral Geniculate Nucleus of Cats and Kittens. J. Neurophysiol. Vol. 78. pp. 1045–1061
DeAngelis G.C., Ohzawa I., Freeman R.D. (1995) Receptive-filed dynamics in the central visual pathways. Trends in Neurosciences (TINS.) Vol. 18(10). pp. 451–458
Dowling J.E., Werblin F.S. (1969) Organization of retina of the mudpuppy, Necturus maculosus. I. Synaptic structure. J Neurophysiol. Vol. 32(3). pp. 315–38
Jacobs A., Werblin F. (1998) Spatiotemporal patterns at the retinal output. J. Neurophysiol. Vol. 80(1), pp. 447–51
Livingstone M.S. (1998) Mechanism of direction selectivity in macaque VI. Neuron Vol. 20. pp. 509–526
Miller J.P., Roska T., Szirányi T., Crounse K.R., Chua L.O., Nemes L. (1994) Deblurring of Images by Cellular Neural Networks with applications to Microscopy. Proceedings of IEEE Int. Workshop on Cellular Neural Networks and Their Applications, (CNNA’94), pp. 237–242
Olshausen B.A. and Field D.J. (1996) Natural Image Statistics and Efficient Coding. Network Computation in Neural Systems Vol. 7(2). pp. 333–339
Orzó L., László K., Négyessy L., Hámori J., Roska T. (1998): Receptive Field Dynamics of the Central Visual Pathway Neurons: Various Definitions and Measurements as well as CNN models. Proc. of CNNA-98. London. pp. 198–203
Roska T., Chua L.O. (1993) The CNN Universal Machine: An Analogic Array Computer, IEEE Transactions on Circuits and Systems-II: Analog and Digital Signal Processing, Vol. 40(3). pp. 163–173
Roska T., Szolgay P., Kozek T., Zarándy, Á., Rekeczky Cs., Nemes L., Kék L., László K., Szatmári I., Csapodi M. (1997) “CADETWIN”, CADETWIN, (CADETWIN), Budapest, MTA SZTAKI
Roska T., Vandewalle J. (eds.) (1993) Cellular Neural Networks. J. Wiley and Sons, Chichester, London, New York
Spillman L. and Warner J.S. (Eds.) (1990) Visual Perception. New York: Academic Press.
Szirányi T. (1996) Robustness of Cellular Neural Networks in Image Deblurring and Texture Segmentation. International Journal of Circuitry and Applications. Vol. 24. pp. 381–396
Werblin F., Roska T. and Chua L.O. (1995) The Analogic Cellular Neural Network as a Bionic Eye. International journal of Circuit Theory and Applications Vol. 23. pp. 541–569
Werblin F.S. (1991) Synaptic connections, receptive fields, and pattern of activity in the tiger salamander retina. Investigative Opthalmology and Visual Science Vol. 32. pp. 459–483
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Orzó, L. (1999). Effects of the ganglion cell response nonlinear mapping on visual system’s noise filtering characteristics. In: Mira, J., Sánchez-Andrés, J.V. (eds) Foundations and Tools for Neural Modeling. IWANN 1999. Lecture Notes in Computer Science, vol 1606. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0098176
Download citation
DOI: https://doi.org/10.1007/BFb0098176
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66069-9
Online ISBN: 978-3-540-48771-5
eBook Packages: Springer Book Archive