Abstract
We show that by evolving neural fields it is possible to study the evolution of neural networks that perform multisensory integration of high dimensional input data. In particular, four simple tasks for the integration of visual and tactile input are introduced. Neural networks evolve that can use these senses in a cost-optimal way, enhance the accuracy of classifying noisy input images, or enhance spatial accuracy of perception. An evolved neural network is shown to display a kind of McGurk effect.
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References
Spence, C.: Multisensory integration, attention and perception. In: Signals and Perception — The Fundamentals of Human Sensation, pp. 345–354. Palgrave Macmillan (2002)
Stein, B.E., Stanford, T.R.: Multisensory integration: current issues from the perspective of the single neuron. Nature Rewievs Neuroscience 9, 255–266 (2008)
Floreano, D., Dürr, P., Mattiussi, C.: Neuroevolution: from architectures to learning. Evolutionary Intelligence 1, 47–62 (2008)
Nolfi, S., Floreano, D.: Evolutionary Robotics — The Biology, Intelligence, and Technology of Self-Organizing Machines. MIT Press (2000)
Inden, B., Jin, Y., Haschke, R., Ritter, H.: Neatfields: Evolution of neural fields. In: Proceedings of the Conference on Genetic and Evolutionary Computation (2010)
Inden, B., Jin, Y., Haschke, R., Ritter, H.: How evolved neural fields can exploit inherent regularity in multilegged robot locomotion tasks. In: Third World Congres on Nature and Biologically Inspired Computation (2011)
Inden, B., Jin, Y., Haschke, R., Ritter, H.: Evolving neural fields for problems with large input and output spaces. Neural Networks 28, 24–39 (2012)
Westermann, G.: A model of perceptual change by domain integration. In: Proceedings of the 23rd Annual Conference of the Cognitive Science Society (2001)
Stanley, K., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evolutionary Computation 10, 99–127 (2002)
Ruppin, E.: Evolutionary autonomous agents: A neuroscience perspective. Nature Reviews Neuroscience 3, 132–141 (2002)
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© 2012 Springer-Verlag Berlin Heidelberg
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Inden, B., Jin, Y., Haschke, R., Ritter, H. (2012). Evolution of Multisensory Integration in Large Neural Fields. In: Hao, JK., Legrand, P., Collet, P., Monmarché, N., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2011. Lecture Notes in Computer Science, vol 7401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35533-2_16
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DOI: https://doi.org/10.1007/978-3-642-35533-2_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35532-5
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