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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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
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