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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De Schutter, E., Ekeberg, Ö., Hellgren Kotaleski, J., Achard, P., Lansner, A.: Biophysically detailed modelling of microcircuits and beyond. Trends Neurosci. 28, 562–569 (2005)
Hirsch, J.A., Martinez, L.M.: Laminar processing in the visual cortical column. Curr. Opin. Neurobiol. 16, 377–384 (2006)
Johansson, C.: An attractor memory model of neocortex. School of Computer Science and Communication. Stockholm, Royal Institute of Technology, Sweden. PhD (2006)
Johansson, C., Lansner, A.: Attractor memory with self-organizing input. In: Ijspeert, A.J., Masuzawa, T., Kusumoto, S. (eds.) BioADIT 2006. LNCS, vol. 3853, pp. 265–280. Springer, Heidelberg (2006)
Johansson, C., Lansner, A.: A hierarchical brain-inspired computing system. In: International Symposium on Nonlinear Theory and its applications (NOLTA), Bologna, Italy, pp. 599–603 (2006b)
Kraskov, A., Stögbauer, H., Andrzejak, R.G., Grassberger, P.: Hierarchical clustering using mutual information. Europhys. Lett. 70(2), 278 (2005)
Lansner, A., Holst, A.: A higher order Bayesian neural network with spiking units. Int. J. Neural Systems 7(2), 115–128 (1996)
LeCun, Y.L., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Leon, M., Johnson, B.A.: Glomerular activity response archive (2006), http://leonserver.bio.uci.edu
Lundqvist, M., Rehn, M., Djurfeldt, M., Lansner, A.: Attractor dynamics in a modular network model of the neocortex. Network: Computation in Neural Systems 17, 253–276 (2006)
Ma, J., Zhao, Y., Ahalt, S., Eads, D.: OSU-SVM for Matlab (2006), http://svm.sourceforge.net
Marco, S., Lansner, A., Gutierrez Galvez, A.: Exploratory analysis of the rat olfactory bulb activity. Abstract. ECRO 2006, Granada, Spain (2006)
Quinlan, P.: Connectionism and psychology. A psychological perspective on connectionist research. New York, Harvester, Whaetsheaf (1991)
Sandberg, A., Lansner, A., Petersson, K.-M., Ekeberg, Ö.: Bayesian attractor networks with incremental learning. Network: Computation in Neural Systems 13(2), 179–194 (2002)
Steinert, R., Rehn, M., Lansner, A.: Recognition of handwritten digits using sparse codes generated by local feature extraction methods. In: 14th European Symposium on Artificial Neural Networks (ESANN) 2006, Brugge, Belgium, pp. 161–166 (2006)
Thorpe, S., Imbert, M.: Biological constraints on connectionist modelling. In: Pfeiffer, R., Berlin, E. (eds.) Connectionism in Perspective. Springer, Berlin (1989)
Ueda, N., Nakano, R.: A new competitive learning approach based on an equidistortion principle for designing optimal vector quantizers. Neural Networks 7(8), 1211–1227 (1994)
Young, F.W.: Multidimensional scaling. Encyclopedia of Statistical Sciences 5, 649–659 (1985)
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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
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