Abstract
This paper presents a new paradigm for signal decomposition and reconstruction that is based on the selection of a sparse set of basis functions. Based on recently reported results, we note that this framework is equivalent to approximating the signal using Support Vector Machines. Two different algorithms of modeling sensory activity within the barrel cortex of a rat are presented. First, a slightly modified approach to the Independent Component Analysis (ICA) algorithm and its application to the investigation of Evoked Potentials (EP), and second, an Evolutionary Algorithm (EA) for learning an overcomplete basis of the EP components by viewing it as probabilistic model of the observed data. The results of the experiments conducted using these two approaches as well as a discussion concerning a possible utilization of those results are also provided.
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References
Haykin, S.: Neural networks: a comprehensive foundation. 3rd edn. Prentice Hall, Upper Saddle River, NJ (1999)
Oja, E.: A simplified neural model as a principal components analyzer. J. of Mathematical Biology 15 (1982) 267–273
Amari, S.-I. and Cichocki, A.: Adaptive blind signal processing-neural network approaches. Proc. of the IEEE 86 (1998) 2026–2048
Barlow, H. B.: Possible principles underlying the transformations of sensory messages. In: Rosenblith, W. A. (ed.): Sensory Communication. MIT Press, Cambridge, MA (1961) 217–234
Raz, J., Dickerson, L., and Turetsky, B.: A Wavelet Packet Model of Evoked Potentials. Brain and Language 66 (1999) 61–88
Chen, S., Donoho, D. L., and Saunders, M. A.: Atomic decomposition by basis pursuit. Technical report, Dept. Stat., Stanford University (1996)
Lewicki, M. and Sejnowski, T.: Learning overcomplete representations. Neural Computation 12 (2000) 337–365
Mallat, S. G. and Zhang, Z.: Matching pursuits with time-frequency dictionaries. IEEE Trans. on Signal Processing 41(12) (1993) 3397–3415
Olshausen, B. and Field, D. J.: Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision Research 37(23) (1997) 3311–3325
Olshausen, B.: Sparse codes and spikes. In: Rao, R. P. N., Olshausen, B. A., Lewicki, M. S. (eds.): Probabilistic Models of Perception and Brain Function. MIT Press, Cambridge, MA (2001)
Milanova, M., Wachowiak, M., Rubin, S., and Elmaghraby, A.: A Perceptual Learning Model on Topological Representation. Proc. of the IEEE International Joint Conference on Neural Networks, Washington, DC, July 15–19 (2001) 406–411
Freeman, W. J.: Measurement of Cortical Evoked Potentials by Decomposition of their Wave Forms. J. of Cybernetics and Information Science, 2-4 (1979) 22–56
Lewicki, M. S. and Olshausen, B. A.: Probabilistic Framework for Adaptation and Comparison of Image Codes. J. Opt. Soc. of Am., 16 (1999) 1587–1600
Girosi, F.: An Equivalence Between Sparse Approximation and Support Vector Machines. Neural Computation 10 (1998) 1455–1480
Vapnik, V.: The nature of Statistical Learning Theory. Springer-Verlag, Berlin Heidelberg New York (1995)
Makeig, S., et al.: ICA Toolbox Tutorial. Available: http://www.cnl.salk.edu/~scott/tutorial/
Field, D. J.: What is the goal of sensory coding? Neural Computation 6 (1994) 559–601
Yoshioka, M. and Omatu, S.: Independent Component Analysis using time delayed sampling. Presented at the IEEE International Joint Conference on Neural Networks, Como, Italy, July 24–27 (2000)
Kublik, E. and Musial, P.: Badanie ukladow czuciowych metoda potencjalow wywolanych (in Polish). Kosmos 46 (1997) 327–336
Wrobel, A., Kublik, E., and Musial, P.: Gating of the sensory activity within barrel cortex of the awake rat. Exp. Brain Res. 123 (1998) 117–123
Kublik, E., Musial, P., and Wrobel, A.: Identification of Principal Components in Cortical Evoked Potentials by Brief Surface Cooling. Clinical Neurophysiology. 112 (2001) 1720–1725
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Milanova, M., Smolinski, T.G., Boratyn, G.M., Zurada, J.M., Wrobel, A. (2002). Sparse Correlation Kernel Analysis and Evolutionary Algorithm-Based Modeling of the Sensory Activity within the Rat’s Barrel Cortex. In: Lee, SW., Verri, A. (eds) Pattern Recognition with Support Vector Machines. SVM 2002. Lecture Notes in Computer Science, vol 2388. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45665-1_16
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DOI: https://doi.org/10.1007/3-540-45665-1_16
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