Abstract
Many tasks performed by humans and animals involve decision-making and behavioral responses to spatiotemporally patterned stimuli. Thus the recognition and processing of time-varying signals is fundamental to a wide range of cognitive processes. Classification of such signals is also basic to many engineering applications such as speech recognition, seismic event detection, sonar classification and real-time control (Lippmann, 1989; Maren, 1990).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ambros-Ingerson, J., Granger, R., and Lynch, G., 1990, Simulation of paleocortex performs hierarchical clustering, Science, 247:1344.
Banzhaf, W. and Kyuma, K., 1991, The time-into-intensity-mapping network, Biol. Cybern., 66:115.
Beck, S., Deuser, L., Still, R., and Whiteley, J., 1991, A hybrid neural network classifier of short duration acoustic signals, Proc. IJCNN, 1:119.
Bell, T., 1988, Sequential processing using attractor transitions, in: “Proceedings of the 1988 Connectionist Models Summer School”, Morgan Kaufmann Publishers, San Mateo, CA.
Braitenberg, V., 1990, Reading the structure of brains, Network, 1:1.
Byrne, J. H. and Gingrich, K. J., 1989, Mathematical model of cellular and molecular processes contributing to associative and nonassociative learning in Aplysia, in: “Neural Models of Plasticity”, J. Byrne, and W. Berry, eds., Academic Press, San Diego, CA.
Chang, H. J. and Ghosh, J., 1993, Pattern association and retrieval in a continuous neural system, Biol Cybern., 69:77.
Changeux, J.-P., Dehaene, S., and Nadal, J.-P., 1987, Neural networks that learn temporal sequences by selection, Proc. Natl. Acad. Sci. USA, 84:2727.
Chen, C., 1985, Automatic recognition of underwater transient signals-a review, Proc. ICASSP, 1270.
Day, S. P. and Davenport, M. R., 1993, Continuous-time temporal back-propagation with adaptable time delays, IEEE Trans Neural Networks, 4:348.
Dayhoff, J., 1990, Regularity properties in pulse transmission networks, Proceedings of the Third International Joint Conference on Neural Networks, 3:621.
de Vries, B. and Principe, J. C., 1990, The gamma model -a new neural net model for temporal processing, Neural Networks, 5:565.
de Vries, B. and Principe, J. C., 1991, A theory for neural networks with time delays, in: “Advances in Neural Information Processing Systems-III”, R.P. Lippmann, J. E. Moody and D. Touretzky, eds., Morgan Kaufmann Publishers, San Mateo, CA.
Deuser, L. and Middleton, D., 1979, On the classification of underwater acoustic signals: An environmentally adaptive approach, J. Acoust. Soc. Am., 65:438.
Djuric, P. M., Kay, S. M., and Boudreaux-Bartels, G. F., 1992, Segmentation of nonstationary signals, in: Proc. ICASSP, 5:161.
Elman, J., 1990, Finding structure in time, Cognit. Sci., 14:179.
Freeman, W. J., Yao, Y., and Burke, B., 1988, Central pattern generating and recognizing in olfactory bulb: a correlation learning rule, Neural Networks, 1:277.
Freitag, L. and Tyack, P., 1993, Passive acoustic localization of the Atlantic bottlenose dolphin using whistles and echolocation clicks, J. Acoust. Soc. Am., 93:2197.
Ghosh, J., 1993, Representation and classification of temporal patterns, in: “Tutorial Notes, ANNIE ’93”, Publisher: Univ. of Missouri-Rolla, St. Louis, Nov. 1993.
Ghosh, J., Deuser, L., and Beck, S., 1990, Impact of feature vector selection on static classification of acoustic transient signals, in: “Government Neural Network Applications Workshop”
Ghosh, J., Deuser, L., and Beck, S., 1992, A neural network based hybrid system for detection, characterization and classification of short-duration oceanic signals, IEEE J. Ocean Engineering, 17:351.
Ghosh, J. and Gangishetti, N., 1993, Robust classification of variable length sonar sequences, Proc. SPIE 1965:96.
Ghosh, J. and Karamcheti, V., 1992, Sequence learning using recurrent networks: Analysis of internal representations, SPIE Proc. 1710:449.
Granger, R., Ambros-Ingerson, J., and Lynch, G., 1991, Derivation of encoding characteristics of layer II cerebral cortex, J. Cognit. Neurosci., 61:78.
Grossberg, S., 1970, Some networks that can learn, remember, and reproduce any number of complicated space-time patterns, II, Stud. App. Math., 49:135.
Haykin, S., 1994, “Neural Networks: A Comprehensive Foundation”, Macmillan, New York, NY.
Hecht-Nielsen, R., 1987, Nearest matched filter classification of spatiotemporal patterns, Applied Optics, 26:1892.
Hecht-Nielsen, R., 1990, “Neurocomputing”, Addison Wesley, Reading, MA.
Hermand, J.-P. and Nicolas, P., 1989, Adaptive classification of underwater transients, Proc. ICASSP, Vol. IV 2712–2715.
Hertz, J., Krogh, A., and Palmer, R. G., 1991, “Introduction to the Theory of Neural Computation”, Addison-Wesley, Reading, MA.
Hopfield, J. and Tank, D., 1989, Neural architecture and biophysics for sequence recognition, in: “Neural Models of Plasticity”, J. Byrne, and W. Berry., eds., Academic Press, San Diego, CA.
Jordan, M., 1989, Serial order: A parallel, distributed processing approach, in: “Advances in Connectionist Theory: Speech”, J. Elman,and D. Rumelhart, eds., Lawrence Erlbaum Associates, Hillsdale, NJ.
Kohonen, T., 1988, The “neural phonetic typewriter”, IEEE Computer, 21:11.
Kohonen, T., 1989, “Self-Organization and Associative Memory”, Springer-Verlag, Berlin.
Kohonen, T., 1990, The self-organizing map, Proc. IEEE, 78:1464.
Kung, S., 1993, “Digital Neural Networks”, Prentice Hall, Englewood Cliffs, NJ.
Kurogi, S., 1987, A model of neural network for spatiotemporal pattern recognition, Biol. Cybern., 57:103.
Lang, K. J., Waibel, A. H., and Hinton, G. E., 1990, A time-delay neural network architecture for isolated word recognition, Neural Networks, 3:23.
Lefebvre, T., Nicolas, J., and Degoul, P., 1990, Numerical to symbolical conversion for acoustic signal classification using a two-stage neural architecture, in: “Proceedings of the International Neural Network Conference, Paris”.
Lin, D., Dayhoff, J. E., and Ligomenides, P. A., 1992, Trajectory recognition with a time-delay neural network, in: “Proceedings of the International Joint Conference on Neural Networks, Baltimore”, 3:197.
Lin, D.-T., Ligomenides, P. A., and Dayhoff, J. E., 1993, Learning spatiotemporal topology using an adaptive time-delay neural network, World Congress on Neural Networks, Oregon, 1:291.
Lippmann, R. P., 1989, Review of neural networks for speech recognition, Neural Computat., 1:1.
Maren, A., 1990, Neural networks for spatio-temporal recognition, in: “Handbook of Neural Computing Applications”, A. Maren, C. Harston, and R. Pap, eds., Academic Press, San Diego, CA.
Mozer, M. C., 1993, Neural network architectures for temporal sequence processing, in: “Time Series Prediction”, A.S. Weigend, and N. Gershenfeld, eds., Addison Wesley, Reading, MA.
Pao, Y., Hemminger, T., Adams, D., and Clary, S., 1991, An episodal neural-net computing approach to the detection and interpretation of underwater acoustic transients, in: “Conference on Neural Networks for Ocean Engineering”, IEEE Press, New York, NY.
Payne, K., Tyack, P., and Payne, R., 1983, Progressive changes in the songs of humpback whales (Megaptera novaegliae), a detailed analysis of two seasons in Hawaii, in: “Behavior and Communication of Whales”, Westview, Boulder, CO.
Pearlmutter, B. A., 1989, Learning state space trajectories in recurrent neural networks, Neural Computat., 1:263.
Pineda, F. J., 1989, Recurrent backpropagation and the dynamical approach to adaptive neural computation, Neural Computat., 1:161.
Principe, J. C., Kuo, J.-M., and Celebi, S., 1994, An analysis of the gamma memory in dynamic neural networks, IEEE Trans. Neural Networks, 5:331.
Rioul, O. and Vetterli, M., 1991, Wavelets and signal processing, IEEE Signal Processing Magazine, 14:38.
Sandberg, I., 1991, Structure theorems for nonlinear systems, Multidimensional Systems and Signal Processing, 2:267.
Sandberg, I., 1992a, Approximately finite memory and input-output maps, IEEE Trans. Circuits Systems, 39:549.
Sandberg, I., 1992b, Approximations for nonlinear functionals, IEEE Trans. Circuits Systems, 39:65.
Sandberg, I., 1994, General structures for classification, IEEE Trans. Circuits Systems, 41:372.
Sato, M., 1990, A real time learning algorithm for recurrent analog neural networks, Biol. Cybern., 62:237.
Sayigh, L., Tyack, P., Wells, R., and Scott, M., 1990, Signature whistles of free-ranging bottlenose dolphins Tursiops truncatus: stability and mother-offspring comparisons, Behav. Ecol. Sociobiol., 26:247.
Shamma, S., 1989, Spatial and temporal processing in central auditory networks, in: “Methods in Neuronal Modeling: From Synapses to Networks”, C. Koch, and I. Segev, eds., MIT Press, Cambridge, MA.
Simpson, P., 1990, Neural networks for SONAR signal processing, in: “Handbook of Neural Computing Applications”, A. Maren, C. Harston, and R. Pap, eds., Academic Press, San Diego, CA.
Stiles, B. and Ghosh, J., 1995, A habituation based mechanism for encoding temporal information in artificial neural networks, Proc. SPIE Vol. 2492, Orlando, April 1995, pp. 404–415.
Sun, G., Chen, H., Lee, Y., and Liu, Y., 1992, Time warping recurrent neural networks and trajectory classification, in: “Proceedings of the International Joint Conference on Neural Networks, Baltimore”, 1:431.
Sutton, R. S., 1988, Learning to predict by the methods of temporal differences, Machine Learning, 3:9.
Tank, D. and Hopfield, J., 1987, Neural computation by time compression, Proc. Natl. Acad. Sci. USA, 84:1896
Urick, R., 1975, “Principles of Underwater Sound”, (2nd Ed.), McGraw-Hill, New York, NY.
Waibel, A., 1989, Modular construction of time-delay neural networks for speech recognition, Neural Computat., 1:39.
Waibel, A. and Hampshire, J., 1989, Building blocks for speech, Byte, pp. 235–242.
Wan, E., 1990, Temporal backpropagation for FIR neural networks, International Joint Conference on Neural Networks, San Diego, 1:575.
Wang, D. and Arbib, M., 1990, Complex temporal sequence learning based on short-term memory, Proc. IEEE, 78:1536.
Wang, D., Buhmann, J., and von der Malsburg, C., 1990, Pattern segmentation in associative memory, Neural Computat, 2:94.
Weigend, A. S. and Gershenfeld, N., eds., 1993, “Time Series Prediction: Forecasting the Future and understanding the past”, Addison Wesley, Reading, MA.
Werbos, P., 1988, Generalization of backpropagation with application to a recurrent gas market model, Neural Networks, 1:339.
Whitehead, H. and Weilgart, L., 1990, Click rates from sperm whales, J. Acoust. Soc. Am., 87:1798.
Widrow, B. and Stearns, S., 1985, “Adaptive Signal Processing”, Prentice-Hall, Englewood Cliffs, NJ.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1995 Springer Science+Business Media New York
About this chapter
Cite this chapter
Ghosh, J., Deuser, L. (1995). Classification of Spatiotemporal Patterns with Applications to Recognition of Sonar Sequences. In: Covey, E., Hawkins, H.L., Port, R.F. (eds) Neural Representation of Temporal Patterns. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1919-5_10
Download citation
DOI: https://doi.org/10.1007/978-1-4615-1919-5_10
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-5785-8
Online ISBN: 978-1-4615-1919-5
eBook Packages: Springer Book Archive