Abstract
It has been almost exactly 10 years since the publication of the Neurocomputing Special Volume on Rough-Neuro Computing, and nearly 8 years since the seminal book “Rough-Neural Computing” came out. Rough-Neuro (or Neural) Computing (RNC) generalizes traditional artificial neural networks by incorporating the concepts of information granularity and computing with words. It provides solid theoretical foundations for hybridization of neural computing with the theory of rough sets, as well as rough mereology, and has many interesting practical applications. Interestingly, while the RNC paradigms directly or indirectly draw extensively from the field of neuroscience, not many applications of the theory of rough sets (in the form of RNC or otherwise) to solve problems in that field exist. This is somewhat surprising as many problems in neuroscience are inherently vague and/or ill-defined and could potentially significantly benefit from the rough sets’ ability to deal with imprecise data, and those applications that have been proposed, have been very successful. In this chapter, we describe a few examples of the existing applications of the theory of rough sets (and its hybridizations) in the field of neuroscience and its clinical “sister,” neurology. We also provide a discussion of other potential applications of rough sets in those areas. Finally, we speculate on how the new insights into the field of neuroscience derived with the help of rough sets may help improve RNC, thus closing the loop between the two fields.
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References
Abbott, L.F.: Lapique’s introduction of the integrate-and-fire model neuron (1907). Brain Research Bulletin 50(5/6), 303–304 (1999)
Blume, W., Lüders, H., Mizrahi, E., Tassinari, C., van Emde Boas, W., Engel, J.: Glossary of descriptive terminology for ictal semiology: report of the ILAE task force on classification and terminology. Epilepsia 42(9), 1212–1218 (2001)
Breedlove, M., Watson, N.V., Rosenzweig, M.R.: Biological Psychology: An Introduction to Behavioral and Cognitive Neuroscience, 5th edn. Sinauer Associates, Inc. (2007)
Czyzewski, A.: Intelligent acquisition of audio signals employing neural networks and rough sets algotithms. In: Pal, S.K., Polkowski, L., Skowron, A. (eds.) Rough-Neural Computing, pp. 521–542. Springer, Heidelberg (2003)
Dayan, P., Abbott, L.F.: Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press (2001)
Dougherty, J., Kohavi, R., Sahami, M.: Supervised and unsupervised discretization of continuous features. In: Proc. 12th International Conference on Machine Learning, Tahoe City, CA, pp. 194–202 (1995)
Fee, M.S., Mitra, P.P., Kleinfeld, D.: Automatic sorting of multiple unit neuronal signals in the presence of anisotropic and non-Gaussian variability. J. Neurosci. Methods 69, 175–188 (1996)
Flury, B.: Common Principal Components and Related Multivariate Models. John Wiley & Sons (1988)
Günay, C., et al.: Computational Intelligence in Electrophysiology: Trends and Open Problems. SCI, vol. 122, pp. 325–359 (2008)
Günay, C., Prinz, A.A.: Model calcium sensors for network homeostasis: Sensor and readout parameter analysis from a database of model neuronal networks. J. Neuroscience 30(5), 1686–1698 (2010)
Hodgkin, A., Huxley, A.: A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117, 500–544 (1952)
Hyvarinen, A., Oja, E.: Independent component analysis: Algorithms and applications. Neural Networks 13, 411–430 (2000)
Johnson, D.S.: Approximation algorithms for combinatorial problems. J. of Computer and System Sciences 9, 256–278 (1974)
Kandel, E.R., Schwartz, J.H., Jessell, T.M.: Principles of Neural Science, 4th edn. McGraw-Hill (2000)
Khorkova, O., Golowasch, J.: Neuromodulators, not activity, control coordinated expression of ionic currents. J. Neuroscience 27(32), 8709–8718 (2007)
Kimble, D.P.: Biological Psychology. Holt, Rinehart, and Winston, Inc. (1988)
Kobashi, S., Kondo, K., Hata, Y.: Rough sets based medical image segmentation with connectedness. In: Proc. 5th Int. Forum on Multimedia and Image Processing, pp. 197–202 (2004)
Komorowski, J., Pawlak, Z., Polkowski, L., Skowron, A.: Rough sets: A tutorial. In: Pal, S.K., Skowron, A. (eds.) Rough Fuzzy Hybridization: A New Trend in Decision-Making, pp. 3–98 (1999)
Kreyszig, E.: Introductory Functional Analysis with Applications. Wiley, New York (1978)
Laumanns, M., Zitzler, E., Thiele, L.: A unified model for multi-objective evolutionary algorithms with elitism. In: Proc. Congress on Evolutionary Computation, pp. 46–53 (2000)
Lawrence, S., Burns, I., Back, A., Tsoi, A.C., Giles, C.L.: Neural Network Classification and Prior Class Probabilities. In: Orr, G.B., Müller, K.-R. (eds.) NIPS-WS 1996. LNCS, vol. 1524, pp. 299–314. Springer, Heidelberg (1998)
Marder, E., Goaillard, J.M.: Variability, compensation and homeostasis in neuron and network function. Nature Reviews Neuroscience 7(7), 563–574 (2006)
Marek, W., Pawlak, Z.: Rough Sets and Information Systems. Fundamenta Informaticae 17, 105–115 (1984)
Milanova, M.G., Smolinski, T.G., Boratyn, G.M., Żurada, J.M., Wrobel, A.: Sparse Correlation Kernel Analysis and Evolutionary Algorithm-Based Modeling of the Sensory Activity within the Rat’s Barrel Cortex. In: Lee, S.-W., Verri, A. (eds.) SVM 2002. LNCS, vol. 2388, pp. 198–212. Springer, Heidelberg (2002)
Miller, J.P., Selverston, A.I.: Mechanisms underlying pattern generation in lobster stomatogastric ganglion as determined by selective inactivation of identified neurons. II. Oscillatory properties of pyloric neurons. J. Neurophysiology 48(6), 1378–1391 (1982)
Morris, C., Lecar, H.: Voltage oscillations in the barnacle giant muscle fiber. Biophys J. 35(1), 193–213 (1981)
Øhrn A.: ROSETTA Technical Reference Manual (2001) (retrieved May 6, 2011), http://www.lcb.uu.se/tools/rosetta/materials/manual.pdf
Pal, S.K, Pedrycz, W., Skowron, A., Swiniarski, R. (eds.): Special Volume: Rough-neuro Computing. Neurocomputing 36 (2001)
Pal, S.K., Polkowski, L., Skowron, A. (eds.): Rough-Neural Computing. Springer (2003)
Orłowska, E., Pawlak, Z.: Representation of nondeterministic information. Theoretical Computer Science 29, 27–39 (1984)
Pawlak, Z.: Rough Sets. International J. of Computer and Information Sciences 11, 341–356 (1982)
Pawlak, Z.: Rough sets - Theoretical aspects of reasoning about data. Kluwer (1991)
Peters, J.F., Skowron, A., Han, L., Ramanna, S.: Towards Rough Neural Computing Based on Rough Membership Functions: Theory and Application. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 611–618. Springer, Heidelberg (2001)
Polkowski, L., Skowron, A.: Rough-Neuro Computing. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 57–64. Springer, Heidelberg (2001)
Prinz, A.A., Abbott, L.F., Marder, E.: The Dynamic Clamp Comes of Age. Trends in Neuroscience 27, 218–224 (2004)
Przybyszewski, A.W.: The Neurophysiological Bases of Cognitive Computation Using Rough Set Theory. In: Peters, J.F., Skowron, A., Rybiński, H. (eds.) Transactions on Rough Sets IX. LNCS, vol. 5390, pp. 287–317. Springer, Heidelberg (2008)
Ropper, A., Samuels, M.: Adams and Victor’s Principles of Neurology, 9th edn. McGraw-Hill Professional (2009)
Schulz, D.J., Goaillard, J.-M., Marder, E.: Quantitative expression profiling of identified neurons reveals cell-specific constraints on highly variable levels of gene expression. PNAS 104(32), 13187–13191 (2007)
Selverston, A.I., Miller, J.P.: Mechanisms underlying pattern generation in lobster stomatogastric ganglion as determined by selective inactivation of identified neurons. I. Pyloric system. J. Neurophysiology 44(6), 1102–1121 (1980)
Simon, R., Greenberg, D., Aminoff, M.: Clinical Neurology, 7th edn. McGraw-Hill Professional (2009)
Skowron, A., Stepaniuk, J.: Tolerance approximation spaces. Fundamenta Informaticae 27, 245–253 (1996)
Smolinski, T.G., Boratyn, G.M., Milanova, M.G., Żurada, J.M., Wrobel, A.: Evolutionary Algorithms and Rough Sets-Based Hybrid Approach to Classificatory Decomposition of Cortical Evoked Potentials. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 621–628. Springer, Heidelberg (2002)
Smolinski, T.G., Chenoweth, D.L., Zurada, J.M.: Time Series Prediction Using Rough Sets and Neural Networks Hybrid Approach. In: Castillo, O. (ed.) Proc. IASTED International Conference on Neural Networks and Computational Intelligence (NCI 2003), pp. 108–111 (2003)
Smolinski, T.G.: Classificatory Decomposition for Time Series Classification and Clustering. PhD thesis, Univ. of Louisville, Louisville (2004)
Smolinski, T.G., Milanova, M.G., Boratyn, G.M., Buchanan, R., Prinz, A.A.: Multi-Objective Evolutionary Algorithms and Rough Sets for Decomposition and Analysis of Cortical Evoked Potentials. In: Proc. IEEE International Conference on Granular Computing (GrC 2006), pp. 635–638 (2006)
Smolinski, T.G., Boratyn, G.M., Milanova, M.G., Buchanan, R., Prinz, A.A.: Hybridization of Independent Component Analysis, Rough Sets, and Multi-Objective Evolutionary Algorithms for Classificatory Decomposition of Cortical Evoked Potentials. In: Rajapakse, J.C., Wong, L., Acharya, R. (eds.) PRIB 2006. LNCS (LNBI), vol. 4146, pp. 174–183. Springer, Heidelberg (2006)
Smolinski, T.G., Soto-Treviño, C., Rabbah, P., Nadim, F., Prinz, A.A.: Analysis of biological neurons via modeling and rule mining. International J. of Information Technology and Intelligent Computing 1(2), 293–302 (2006)
Smolinski, T.G., Soto-Treviño, C., Rabbah, P., Nadim, F., Prinz, A.A.: Computational exploration of a multi-compartment model of the AB neuron in the lobster pyloric pacemaker kernel. BMC Neuroscience 9(suppl. 1), P53 (2008)
Smolinski, T.G., Prinz, A.A.: Rough Sets for Solving Classification Problems in Computational Neuroscience. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds.) RSCTC 2010. LNCS (LNAI), vol. 6086, pp. 620–629. Springer, Heidelberg (2010)
Soto-Treviño, C., Rabbah, P., Marder, E., Nadim, F.: Computational model of electrically coupled, intrinsically distinct pacemaker neurons. J. Neurophysiology 94(2), 590–604 (2005)
Sulaiman, S., Shamsuddin, S.M., Abraham, A.: Rough Neuro-PSO Web Caching and XML Prefetching for Accessing Facebook from Mobile Environment. In: Proc. 8th International Conference on Computer Information Systems and Industrial Management (CISIM 2009), pp. 884–889. IEEE Computer Society Press (2009)
Szczuka, M., Wojdyłło, P.: Neuro-Wavelet Classifiers for EEG Signals Based on Rough Set Methods. In: Pal, S.K., Pedrycz, W., Skowron, A., Swiniarski, R. (eds.) Special Volume: Rough-neuro Computing. Neurocomputing, vol. 36, pp. 103–122 (2001)
Tsumoto, S.: Computational Analysis of Acquired Dyslexia of Kanji Characters Based on Conventional and Rough Neural Networks. In: Pal, S.K., Polkowski, L., Skowron, A. (eds.) Rough-Neural Computing, pp. 637–648. Springer, Heidelberg (2003)
Tulinius, M.H., Holme, E., Kristianson, B.: Mitochondrial encephalomyopathies in childhood: 1. Biochemical and morphologic investigations. J. Pediatrics 119, 242–250 (1991)
Tulinius, M.H., Holme, E., Kristianson, B.: Mitochondrial encephalomyopathies in childhood: 2. Clinical manifestation and syndromes. J. Pediatrics 119, 251–259 (1991)
Vinterbo, S., Øhrn, A.: Minimal approximate hitting sets and rule templates. International J. of Approximate Reasoning 25(2), 123–143 (2000)
Wakulicz-Deja, A., Paszek, P.: Applying rough set theory to multi stage medical diagnosing. Fundamenta Informaticae 54(4), 387–408 (2003)
Widz, S., Revett, K., Ślęzak, D.: Application of rough set based dynamic parameter optimization to MRI segmentation. In: Proc. 23rd Int. Conference of the North American Fuzzy Information Processing Society, pp. 440–445 (2004)
Wróblewski, J.: Finding minimal reducts using genetic algorithms. In: Proc. 2nd Annual Joint Conference on Information Sciences, pp. 186–189 (1995)
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Smolinski, T.G., Prinz, A.A. (2013). Rough Sets and Neuroscience. In: Skowron, A., Suraj, Z. (eds) Rough Sets and Intelligent Systems - Professor Zdzisław Pawlak in Memoriam. Intelligent Systems Reference Library, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30341-8_26
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