References - Biomedical Signal Analysis: Contemporary Methods and Applications - page 404

211(1):101-110, 1999.
[146]H. Kuhn, H. Dickinson-Anson, and F. Gage. Neurogenesis in the dentate
gyrus of the adult rat: Age-related decrease of neuronal progenitor proliferation. J.
Neurosci. , 16(6):2027-2033, 1996.
[147]H. Kuhn, T. Palmer, and E. Fuchs. Adult neurogenesis: A compensatory
mechanism for neuronal damage. Eur. Arch. Psychiatry Clin. Neurosci. ,
251(4):152-158, 2001.
[148]O. Lange, A. Meyer-Baese, M. Hurdal, and S. Foo. A comparison between
neural and fuzzy cluster analysis techniques for functional MRI. Biomedical Signal
Processing and Control , 1(3):243-252, 2006.
[149]N. Lassen and W. Perl. Tracer Kinetic Methods in Medical Physiology .Raven
Press, New York, 1979.
[150]D. Lee and H. Seung. Learning the parts of objects by non-negative matrix
factorization. Nature , 401:788-791, 1999.
[151]S. Lee and R. M. Kil. A Gaussian Potential Function Network with
Hierarchically Self-Organizing Learning. Neural Networks , 4(9):207-224, 1991.
[152]T. Lee, M. Girolami, and T. Sejnowski. Independent component analysis using
an extended infomax algorithm for mixed sub-Gaussian and super-Gaussian
sources. Neural Computation , 11:417-441, 1999.
[153]C. Leondes. Image Processing and Pattern Recognition . Academic Press,
1998.
[154]A. Levin, A. Zomet, S. Peleg, and Y. Weiss. Seamless Image Stitching in the
Gradient Domain. Technical Report 2003-82, Leibniz Center, Hebrew University,
Jerusalem, 2003.
[155]J. Lin. Factorizing multivariate function classes. In Advances in Neural
Information Processing Systems , volume 10, pages 563-569. MIT Press, 1998.
[156]Y. Linde, A. Buzo, and R. Gray. An algorithm for vector quantizer design.
IEEE Transactions on Communications , 28(3):84-95, 1980.
[157]R. Linsker. An application of the principle of maximum information
preservation to linear systems. Advances in Neural Information Processing
Systems , 1, MIT Press, 1989.
[158]R. Linsker. Local synaptic learning rules suce to maximize mutual
information in a linear network. Neural Computation , 4:691-702, 1992.
[159]R. P. Lippman. An introduction to computing with neural networks. IEEE
ASSP Magazine , 4(4):4-22, 1987.
[160]Lo, Leung, and Litva. Separation of a mixture of chaotic signals. In Proc. Int.
Conf. Accustics, Speech and Signal Processing , pages 1798-1801, 1996.
[161]E. Lucht, S. Delorme, and G. Brix. Neural network-based segmentation of
dynamic (MR) mammography images. Magnetic Resonance Imaging , 20(8):89-94,
2002.
[162]E. Lucht, M. Knopp, and G. Brix. Classification of signal-time curves from
dynamic (MR) mammography by neural networks. Magnetic Resonance Imaging ,
19(8):51-57, 2001.
[163]D. MacKay. Information Theory, Inference, and Learning Algorithms .6th
ed. Cambridge University Press, 2003.
[164]A. Macovski. Medical Imaging Systems . Prentice Hall, 1983.
[165]S. Mallat. A Wavelet Tour of Signal Processing . Academic Press, 1997.
[166]T. Martinetz, S. Berkovich, and K. Schulten. Neural gas network for vector
quantization and its application to time-series prediction. IEEE Transactions on