Abstract
In living organisms, the morphology of sensory organs and the behavior of a sensor’s host are strongly tied together. For visual organs, this interrelationship is heavily influenced by the spatial topology of the sensor and how it is moved with respect to an organism’s environment. Here we present a computational approach to the organization of spatial layouts of visual sensors according to given sensor-environment interaction patterns. We propose that prediction and spatiotemporal correlation are key principles for the development of visual sensors well-adapted to an agent’s interaction with its environment. This proposition is first motivated by studying the interdependency of morphology and behavior of a number of visual systems in nature. Subsequently, we encode the characteristics observed in living organisms by formulating an optimization problem which maximizes the average spatiotemporal correlation between actual and predicted stimuli. We demonstrate that the proposed formulation leads to spatial self-organization of visual receptive fields, and leads to different sensor topologies according to different sensor displacement patterns. The obtained results demonstrate the explanatory power of our approach with respect to i) the development of spatially coherent light receptive fields on a visual sensor surface, and ii) the particular topological organization of receptive fields depending on sensorimotor activity.
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References
Hayhoe, M., Ballard, D.: Eye movements in natural behavior. Trends in Cognitive Sciences 9, 188–194 (2005)
Egelhaaf, M., Kern, R., Krapp, H.G., Kretzberg, J., Kurtz, R., Warzecha, A.K.: Neural enconding of behaviourally relevant visual-motion information in the fly. Trends in Neurosciences 25, 96–102 (2002)
Schwartz, E.L.: Computational anatomy and functional architecture of striate cortex: A spatial mapping approach to perceptual coding. Vision Research 20(1), 645–669 (1980)
Petrowitz, R., Dahmen, H., Egelhaaf, M., Krapp, H.G.: Arrangement of optical axes and spatial resolution in the compound eye of the female blowfly Calliphora. J. Comp. Physiology A 186, 737–746 (2000)
Lichtensteiger, L., Eggenberger, P.: Evolving the morphology of a compound eye on a robot. In: Proc. 3rd Europ. Worksh. on Adv. Mobile Robots, pp. 127–134 (1999)
Clippingdale, S.M., Wilson, R.: Self-similar neural networks based on a kohonen learning rule. Neural Networks 9(5), 747–763 (1996)
Ruesch, J., Ferreira, R., Bernardino, A.: A measure of good motor actions for active visual perception. In: Proc. Int. Conf. Dev. and Learning, ICDL (2011)
Ruesch, J., Ferreira, R., Bernardino, A.: Predicting visual stimuli from self-induced actions: an adaptive model of a corollary discharge circuit. IEEE Transactions on Autonomous Mental Development (submitted)
Crapse, T.B., Sommer, M.A.: Corollary discharge across the animal kingdom. Nat. Rev. Neuroscience 9, 587–600 (2008)
Absil, P.A., Mahoney, R., Sepulchre, R.: Optimization Algorithms on Matrix Manifolds. Princeton University Press (2008)
Barzilai, J., Borwein, J.: Two-point step size gradient methods. IMA Journal of Numerical Analysis 8, 141–148 (1988)
Bertsekas, D.P.: Constrained Optimization and Lagrange Multiplier Methods. Athena Scientific (1996)
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Ruesch, J., Ferreira, R., Bernardino, A. (2012). Self-organization of Visual Sensor Topologies Based on Spatiotemporal Cross-Correlation. In: Ziemke, T., Balkenius, C., Hallam, J. (eds) From Animals to Animats 12. SAB 2012. Lecture Notes in Computer Science(), vol 7426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33093-3_26
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DOI: https://doi.org/10.1007/978-3-642-33093-3_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33092-6
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