Abstract
Artificial neural networks (ANN) are useful components in today’s data analysis toolbox. They were initially inspired by the brain but are today accepted to be quite different from it. ANN typically lack scalability and mostly rely on supervised learning, both of which are biologically implausible features. Here we describe and evaluate a novel cortex-inspired hybrid algorithm. It is found to perform on par with a Support Vector Machine (SVM) in classification of activation patterns from the rat olfactory bulb. On-line unsupervised learning is shown to provide significant tolerance to sensor drift, an important property of algorithms used to analyze chemo-sensor data. Scalability of the approach is illustrated on the MNIST dataset of handwritten digits.
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Lansner, A., Benjaminsson, S., Johansson, C. (2009). From ANN to Biomimetic Information Processing. In: Gutiérrez, A., Marco, S. (eds) Biologically Inspired Signal Processing for Chemical Sensing. Studies in Computational Intelligence, vol 188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00176-5_2
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DOI: https://doi.org/10.1007/978-3-642-00176-5_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00175-8
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