Dually optimal neuronal layers: Lobe component analysis

IEEE Transactions on Autonomous Mental Development

Volumn 1, Issue 1, 2009, Pages 68-85

  Weng, Juyang ^a   Luciw, Matthew ^a  

^a Michigan State University   (United States)

Author keywords

Blind source separation; Cortical models; Feature extraction; Hebbian learning; Optimality; Plasticity

Indexed keywords

ADAPTIVE LEARNING ALGORITHM; COMPETITIVE ADVANTAGE; COMPONENT ANALYSIS; CONNECTED NETWORKS; CORTICAL MODELS; FAST LEARNING; GRADIENT BASED; HEBBIAN; HEBBIAN LEARNING; HIGH CONCENTRATION; HIGHER ORDER STATISTICS; INDEPENDENT COMPONENTS; INPUT SPACE; LARGE SIZES; LEARNING SPEED; MATHEMATICAL ANALYSIS; OPTIMALITY; PROBABILITY DENSITIES; REAL-WORLD; SEPARATE LEARNING;

ADAPTIVE ALGORITHMS; APARTMENT HOUSES; BLIND SOURCE SEPARATION; CELL MEMBRANES; COMPETITION; COMPUTATIONAL COMPLEXITY; EDUCATION; FEATURE EXTRACTION; INDEPENDENT COMPONENT ANALYSIS; LEARNING SYSTEMS; LIFE CYCLE; PLASTICITY; PROBABILITY DENSITY FUNCTION; SEPARATION; SIGNAL ANALYSIS;

LEARNING ALGORITHMS;

EID: 71049175500     PISSN: 19430604     EISSN: None     Source Type: Journal    
DOI: 10.1109/TAMD.2009.2021698     Document Type: Article

References (51)

1. J. L. Elman, E. A. Bates, M. H. Johnson, A. Karmiloff-Smith, D. Parisi, and K. Plunkett, Rethinking Innateness: A Connectionist Perspective on Development. Cambridge, MA: MIT Press, 1997.
2. J. Weng, J. McClelland, A. Pentland, O. Sporns, I. Stockman, M. Sur, and E. Thelen, "Autonomous mental development by robots and animals," Science, vol. 291, no. 5504, pp. 599-600, 2001.
3. J. Weng and I. Stockman, "Autonomous mental development: Workshop on development and learning," AI Mag., vol. 23, no. 2, pp. 95-98, 2002.
4. R. Miikkulainen, J. A. Bednar, Y. Choe, and J. Sirosh, Computational Maps in the Visual Cortex. Berlin, Germany: Springer, 2005.
5. G. J. McLachlan, The EM Algorithm and Extensions. New York: Wiley, 1997.
6. J. Weng and N.Zhang, "In-place learning and the lobe component analysis," in Proc. IEEE World Congr. Comput. Intell., Vancouver, BC, Canada, Jul. 16-21, 2006.
7. J. Weng, T. Luwang, H. Lu, and X. Xue, "A multilayer in-place learning network for development of general invariances," Int. J. Human. Robot., vol. 4, no. 2, pp. 281-320, 2007.
8. M. D. Luciw and J. Weng, "Topographic class grouping and its applications to 3d object recognition," in Proc. IEEE/INNS Int. Joint Conf. Neural Netw., Hong Kong SAR, China, 2008.
9. J. Weng, T. Luwang, H. Lu, and X. Xue, "Multilayer in-place learning networks for modeling functional layers in the laminar cortex," Neural Netw., vol. 21, pp. 150-159, 2008.
10. D. J. Felleman and D. C. Van Essen, "Distributed hierarchical processing in the primate cerebral cortex," Cerebral Cortex, vol. 1, pp. 1-47, 1991.
11. E. M.Callaway, "Local circuits in primary visual cortex ofthe macaque monkey," Annu. Rev. Neurosci., vol. 21, pp. 47-74, 1998.
12. A. K. Wiser and E. M. Callaway, "Contributions of individual layer 6 pyramidal neurons to local circuitry in macaque primary visual cortex," J. Neurosci., vol. 16, pp. 2724-2739, 1996.
13. S. Grossberg and R. Raizada, "Contrast-sensitive perceptual grouping and object-based attention in the laminar circuits of primary visual cortex," Vision Res., vol. 40, pp. 1413-1432, 2000.
14. S. Grossberg, "Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors," Biol. Cybern., vol. 23, pp. 121-131, 1976.
15. G. A. Carpenter and S. Grossberg, "A massively parallel architecture for a self-organizing neural pattern recognition machine," Comput. Vision, Graph., Image Process., vol. 37, pp. 54-115, 1987.
16. , E. R. Kandel, J. H. Schwartz, and T. M. Jessell, Eds., Principles of Neural Science, 4th ed. New York: McGraw-Hill, 2000.
17. M. Kirby and L. Sirovich, "Application of the Karhunen-Loéve procedure for the characterization of human faces," IEEE Trans. Pattern Anal. Machine Intell., vol. 12, pp. 103-108, Jan. 1990.
18. M. Turk and A. Pentland, "Eigenfaces for recognition," J. Cogn. Neurosci., vol. 3, no. 1, pp. 71-86, 1991.
19. K. Etemad and R. Chellappa, "Discriminant analysis for recognition of human face images," in Proc. Int. Conf. Acoust., Speech, Signal Process., Atlanta, GA, May 1994, pp. 2148-2151.
20. D. L. Swets and J. Weng, "Using discriminant eigenfeatures for image retrieval," IEEE Trans. Pattern Anal. Machine Intell., vol. 18, no. 8, pp. 831-836, 1996.
21. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, "Eigenfaces vs fisherfaces: Recognition using class specific linear projection," IEEE Trans. Pattern Anal. Machine Intell., vol. 19, pp. 711-720, Jul. 1997.
22. A. Hyvarinen and E. Oja, "A fast fixed-point algorithm for independent component analysis," Neural Comput., vol. 9, no. 7, pp. 1483-1492, 1997.
23. A. Hyvarinen and E. Oja, "Independent component analysis: Algorithms and applications," Neural Netw., vol. 13, no. 4-5, pp. 411-430, 2000.
24. A. J. Bell and T. J. Sejnowski, "The 'independent components' of natural scenes are edgefilters," Vision Res., vol. 37, no. 23, pp. 3327-3338, 1997.
25. T. W. Lee, M. Girolami, and T. J. Sejnowski, "Independent component analysis using an extended infomax algorithm for mixed sub-Gaussian and super-Gaussian sources," Neural Comput., vol. 11, no. 2, pp. 417-441, 1999.
26. J. Karhunen and P. Pajunen, "Blind source separation using least-squares type adaptive algorithms," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., Munich, Germany, 1997, pp. 3048-3051.
27. G. A. Carpenter, S. Grossberg, and D. B. Rosen, "Fuzzy art: Fast stable learning and categorization of analog patterns by an adaptive resonance system," Neural Netw., vol. 4, pp. 759-771, 1991.
28. J. Weng, Y. Zhang, and W. Hwang, "Candid covariance-free incremental principal component analysis," IEEE Trans. Pattern Anal. Machine Intell., vol. 25, no. 8, pp. 1034-1040, 2003.
29. M. B. Feller, D. P. Wellis, D. Stellwagen, F. S. Werblin, and C. J. Shatz, "Requirement for cholinergic synaptic transmission in the propagation of spontaneous retinal waves," Science, vol. 272, no. 5265, pp. 1182-1187, 1996.
30. J. C. Crowley and L. C. Katz, "Development of cortical circuits: Lessons from ocular dominance columns," Nature Rev. Neurosci., vol. 3, pp. 34-42, 2002.
31. C. W. Cotman and M. Nieto-Sampedro, "Cell biology of synaptic plasticity," Science, vol. 225, pp. 1287-1294, 1984.
32. W. K. Purves, D. Sadava, G. H. Orians, and H. C. Heller, Life: The Science of Biology, 7th ed. Sunderland, MA: Sinauer, 2004.
33. B. W. Silverman, Density Estimation for Statistics and Data Analysis. London, U.K.: Chapman and Hall, 1986.
34. I. T. Jolliffe, Principal Component Analysis. New York: Springer-Verlag, 1986.
35. Y. Tang, J. R. Nyengaard, D. M. De Groot, and H. J. Gundersen, "Total regional and global number of synapses in the human brain neocortex," Synapse, vol. 41, no. 3, pp. 258-273, 2001.
36. E. L. Lehmann, Theory of Point Estimation. New York: Wiley, 1983.
37. J. Weng, T. S. Huang, and N. Ahuja, Motion and Structure From Image Sequences. New York: Springer-Verlag, 1993.
38. A. Papoulis, Probability, Random Variables, and Stochastic Processes, 2nd ed. New York: McGraw-Hill, 1976.
39. P. Dayan and L. F. Abbott, Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. Cambridge, MA: MIT Press, 2001.
40. J. Hertz, A. Krogh, and R. G. Palmer, Introduction to the Theory of Neural Computation. New York: Addison-Wesley, 1991.
41. E.Oja, "A simplified neuron model as a principal component analyzer," J. Math Biol., vol. 15, pp. 267-273, 1982.
42. T. Kohonen, Self-Organizing Maps, 3rd ed. Berlin, Germany: Springer-Verlag, 2001.
43. E. Simoncelli and B. Olshausen, "Natural image statistics and neural representation," Annu. Rev. Neurosci., vol. 24, pp. 1193-1216, 2001.
44. E. Alhoniemi, J. Vesanto, J. Himberg, and J. Parhankangas, Som toolbox for Matlab 5 Helsinki Univ. of Technol., Finland, Tech. Rep. A57, 2000.
45. M. D. Luciw, J. Weng, and S. Zeng, "Motor initiated expectation through top-down connections as abstract context in a physical world," in Proc. 7th Int. Conf. Develop. Learn. (ICDL'08), Monterey, CA, 2008.
46. P. Pajunen and J. Karhunen, "Least-squares methods for blind source separation based on nonlinear PCA," Int. J. Neural Syst., vol. 8, no. 5-6, pp. 601-612, 1998.
47. J. Karhunen, P. Pajunen, and E. Oja, "The nonlinear PCA criterion in blind source separation: Relations with other approaches," Neurocomputing, vol. 22, pp. 5-20, 1998.
48. X. Giannakopoulos, J. Karhunen, and E. Oja, "Experimental comparison of neural ICA algorithms," in Proc. Int. Conf. Artif. Neural Netw. (ICANN'98), Skövde, Sweden, 1998, pp. 651-656.
49. J.-F. Cardoso, "Blind signal separation: Statistical principles," Proc. IEEE, vol. 86, no. 10, pp. 2009-2025, 1998.
50. A. Hyvarinen, J. Karhunen, and E. Oja, Independent Component Analysis. New York: Wiley, 2001.
51. P. J. Huber, Robust Statistics. New York: Wiley, 1981.