Connectionism: A Hands-On Approach

Connectionism: A Hands-On Approach

Volumn , Issue , 2008, Pages 1-200

  Dawson, Michael R W ^a  

^a NONE

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EID: 84889822618     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9780470694077     Document Type: Book

References (157)

1. Aldenderfer, M. S. & Blashfield, R. K. (1984). Cluster Analysis, vol. 07-044. Beverly Hills, CA: Sage Publications.
2. Anderson, J. A. & Rosenfeld, E. (1998). Talking Nets: An Oral History of Neural Networks. Cambridge, MA:MIT Press.
3. Ballard, D. (1986). Cortical structures and parallel processing: Structure and function. The Behavioral And Brain Sciences, 9, 67-120.
4. Barlow, H. B. (1972). Single units and sensation: A neuron doctrine for perceptual psychology? Perception. 1, 371-94.
5. Berkeley, I. S. N., Dawson, M. R. W., Medler, D. A., Schopflocher, D. P., & Hornsby, L. (1995). Density plots of hidden value unit activations reveal interpretable bands. Connection Science, 7, 167-86.
6. Bever, T. G., Fodor, J. A., & Garrett, M. (1968). A formal limitation of associationism. In T. R. Dixon & D. L. Horton (eds), Verbal Behavior and General Behavior Theory (pp. 582-5). Englewood Cliffs, NJ:Prentice Hall.
7. Blake, C. L. & Merz, C. J. (1998). UCI Repository of machine learning databases. Irvine, CA: University of California, Department of Information and Computer Science. Available: [http://www.ics.uci.edu/~mlearn/MLRepository.html].
8. Blyth, T. S. & Robertson, E. F. (1986). Matrices and Vector Spaces, vol. 2. London: Chapman and Hall.
9. Boole, G. (1854). An investigation of the laws of thought, on which are founded the mathematical theories of logic and probabilities. London: Walton and Maberley.
10. Boole, G. (1952). Studies in logic and probability: Watts.
11. Braitenberg, V. (1984). Vehicles: Explorations In Synthetic Psychology. Cambridge, MA: MIT Press.
12. Brown, T. H. (1990). Hebbian synapses: Biophysical mechanisms and algorithms. Annual Review of Neuroscience, 13, 475-511.
13. Calkins, M. W. (1894). Association. Psychological Review, 1, 476-83.
14. Calvin, W. H. (1996a). The Cerebral Code. Cambridge, MA: MIT Press.
15. Calvin, W. H. (1996b). How Brains Think. New York, NY: Basic Books.
16. Carpenter, G. A. (1989). Neural network models for pattern recognition and associative memory. Neural Networks, 2, 243-57.
17. Caudill, M. & Butler, B. (1992a). Understanding neural networks, vol. 1. Cambridge, MA: MIT Press.
18. Caudill, M. & Butler, B. (1992b). Understanding neural networks, vol. 2. Cambridge, MA: MIT Press.
19. Changeux, J.-P. & Dehaene, S. (1993). Neuronal models of cognitive functions. In M. H. Johnson (ed.), Cognition and Brain Development: A reader (pp. 363-402). Oxford: Blackwell.
20. Clark, A. (1989). Microcognition. Cambridge, MA: MIT Press.
21. Clark, A. (1993). Associative engines. Cambridge, MA: MIT Press.
22. Cotman, C. W., Monaghan, D. T., & Ganong, A. H. (1988). Excitatory amino acid neurotransmission:NMDA receptors and Hebb-type synaptic plasticity. Annual Review of Neuroscience, 11, 61-80.
23. Cotter, N. E. (1990). The Stone-Weierstrass theorem and its application to neural networks. IEEE Transactions on Neural Networks, 1, 290-5.
24. Cummins, R. (1983). The Nature of Psychological Explanation. Cambridge, MA.: MIT Press.
25. Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems, 2, 303-314.
26. Cziko, G. (1995). Without Miracles: Universal Selection Theory and the Second Darwinian Revolution. Cambridge, MA: MIT Press.
27. Dallett, K. M. (1966). Effects of within-list and between-list acoustic similarity on the learning and retention of paired associates. Journal of Experimental Psychology, 72(5), 667-77.
28. Dawson, M. R. W. (1991). The how and why of what went where in apparent motion: Modeling solutions to the motion correspondence process. Psychological Review, 98, 569-603.
29. Dawson, M. R. W. (1998). Understanding Cognitive Science. Oxford, UK: Blackwell.
30. Dawson, M. R. W. (2002). From embodied cognitive science to synthetic psychology. In Wang, Y., Johnston, R. H. & Smith, M. R. (eds) Proceedings of the First IEEE International Conference on Cognitive Informatics (ICCI'02). IEEE Computer Society: Los Alamitos, CA, (pp. 13-22)
31. Dawson, M. R. W. (2004). Minds And Machines: Connectionism and Psychological Modeling. Malden, MA:Blackwell Publishing.
32. Dawson, M. R. W., Boechler, P. M., & Valsangkar-Smyth, M. (2000). Representing space in a PDP network:Coarse allocentric coding can mediate metric and nonmetric spatial judgements. Spatial Cognition and Computation, 2, 181-218.
33. Dawson, M. R. W., Kremer, S. & Gannon, T. (1994). Identifying the trigger features for hidden units in a PDP model of the early visual pathway. In R. Elio (ed.), Tenth Canadian Conference On Artificial Intelligence (pp. 115-19). San Francisco, CA: Morgan Kaufmann.
34. Dawson, M. R. W. & Medler, D. A. (1996). Of mushrooms and machine learning: Identifying algorithms in a PDP network. Canadian Artificial Intelligence, 38, 14-17.
35. Dawson, M. R. W., Medler, D. A., & Berkeley, I. S. N. (1997). PDP networks can provide models that are not mere implementations of classical theories. Philosophical Psychology, 10, 25-40.
36. Dawson, M. R. W., Medler, D. A., McCaughan, D. B., Willson, L., & Carbonaro, M. (2000). Using extra output learning to insert a symbolic theory into a connectionist network. Minds and Machines, 10, 171-201.
37. Dawson, M. R. W. & Piercey, C. D. (2001). On the subsymbolic nature of a PDP architecture that uses a nonmonotonic activation function. Minds and Machines, 11, 197-218.
38. Dawson, M. R. W. & Pylyshyn, Z. W. (1988). Natural constraints on apparent motion. In Z. W. Pylyshyn (ed.), Computational Processes in Human Vision: An Interdisciplinary Perspective (pp. 99-120). Norwood, NJ: Ablex.
39. Dawson, M. R. W. & Schopflocher, D. P. (1992a). Autonomous processing in PDP networks. Philosophical Psychology, 5, 199-219.
40. Dawson, M. R. W. & Schopflocher, D. P. (1992b). Modifying the generalized delta rule to train networks of nonmonotonic processors for pattern classification. Connection Science, 4, 19-31.
41. Dawson, M. R. W. & Shamanski, K. S. (1994). Connectionism, confusion and cognitive science. Journal of Intelligent Systems, 4, 215-62.
42. Dawson, M. R. W. & Zimmerman, C. (2003). Interpreting the internal structure of a connectionist model of the balance scale task. Brain and Mind, 4, 129-49.
43. De Wilde, P. (1997). Neural Network Models, 2nd edn. London: Springer.
44. Deese, J. & Hulse, S. H. (1967). The Psychology Of Learning, 3rd edn. New York, NY: McGraw-Hill.
45. Delamater, A. R., Sosa, W., & Koch, M. (1999). Elemental and configural processes in patterning discrimination learning. The Quarterly Journal Of Experimental Psychology, 52B, 97-124.
46. Duch, W. & Jankowski, N. (1999). Survey of neural transfer functions. Neural Computing Surveys, 2, 163-212.
47. Dutton, J. M. & Starbuck, W. H. (1971). Computer simulation of human behavior. New York: John Wiley & Sons.
48. Edelman, G. M. (1987). Neural Darwinism. New York: Basic Books.
49. Edelman, G. M. (1988). Topobiology. New York: Basic Books.
50. Edelman, G. M. (1989). The Remembered Present. New York: Basic Books.
51. Edelman, G. M. (1992). Bright Air, Brilliant Fire. New York: Basic Books.
52. Eich, J. M. (1982). A composite holographic associative recall model. Psychological Review, 89, 627-61.
53. Eichenbaum, H. (1992). The Hippocampal System and Declarative Memory in Animals. Journal of Cognitive Neuroscience, 4(3), 217-31.
54. Eichenbaum, H. (2000). A cortical-hippocampal system for declarative memory. Nature Reviews Neuroscience, 1(1), 41-50.
55. Eichenbaum, H., Dudchenko, P., Wood, E., Shapiro, M., & Tanila, H. (1999). The hippocampus, memory, and place cells: Is it spatial memory or a memory space? Neuron, 23(2), 209-26.
56. Everitt, B. (1980). Cluster Analysis. New York: Halsted.
57. Fodor, J. A. & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture. Cognition, 28, 3-71.
58. Freeman, J. A. (1994). Simulating Neural Networks With Mathematica. Reading, MA: Addison-Wesley.
59. Funahashi, K. (1989). On the approximate realization of continuous mappings by neural networks. Neural Networks, 2, 183-92.
60. Furumoto, L. (1980). Mary Whiton Calkins (1863-1930). Psychology of Women Quarterly, 5, 55-68.
61. Gallant, S. I. (1993). Neural network learning and expert systems. Cambridge, MA: MIT Press.
62. Gallistel, C. R. (1990). The Organization of Learning. Cambridge, MA: MIT Press.
63. Girosi, F. & Poggio, T. (1990). Networks and the best approximation property. Biological Cybernetics, 63, 169-76.
64. Gorsuch, R. L. (1983). Factor Analysis, 2nd edn. Hillsdale, NJ: Lawrence Erlbaum Associates.
65. Hanson, S. J. & Burr, D. J. (1990). What connectionist models learn: Learning and representation in connectionist networks. Behavioral and Brain Sciences, 13, 471-518.
66. Hartman, E., Keeler, J. D., & Kowalski, J. M. (1989). Layered neural networks with Gaussian hidden units as universal approximation. Neural Computation, 2, 210-15.
67. Hawkins, R. D. B. & Bower, G. H. (1989). Computational models of learning in simple neural systems. San Diego: Academic Press.
68. Hebb, D. O. (1949). The Organization Of Behavior. New York: John Wiley & Sons.
69. Hertz, J., Krogh, A., & Palmer, R. G. (1991). Introduction to the Theory of Neural Computation. Redwood City, CA: Addison-Wesley.
70. Hillis, W. D. (1988). Intelligence as emergent behavior, or, the songs of Eden. In S. R. Graubard (ed.), The Artificial Intelligence Debate (pp. 175-89). Cambridge, MA: MIT Press.
71. Hinton, G. E. (1986). Learning distributed representations of concepts. Paper presented at the 8th Annual Meeting of the Cognitive Science Society, Ann Arbor, MI.
72. Hinton, G. E. & Anderson, J. A. (1981). Parallel Models Of Associative Memory. Hillsdale, NJ: Lawrence Erlbaum Associates.
73. Honda, M., Barrett, G., Yoshimura, N., Sadato, N., Yonekura, Y., & Shibasaki, H. (1998). Comparative study of event-related potentials and positron emission tomography activation during a paired-associate memory paradigm. Experimental Brain Research, 119(1), 103-15.
74. Hornik, M., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2, 359-66.
75. Hunt, R. G. (1959). Meaningfulness and articulation of stimulus and response in paired-associate learning and stimulus recall. Journal of Experimental Psychology, 57(4), 262-7.
76. James, W. (1890). The Principles Of Psychology, Volume One. New York, NY: Dover Publications.
77. Jerne, N. K. (1967). Antibodies and learning: Selection versus instruction. In G. C. Quarton, T. Melnechuk & F. O. Schmitt (eds), The Neurosciences: A study program (pp. 200-208). New York:Rockefeller University Press.
78. Jevons, W. S. (1971). Pure logic, and other minor works. B. Franklin.
79. Kahana, M. J. (2002). Associative symmetry and memory theory. Memory & Cognition, 30(6), 823-40.
80. Kasabov, N. K. (1996). Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering. Cambridge, MA: MIT Press.
81. Kehoe, E. J. (1988). A layered network model of associative learning: Learning to learn and configuration. Psychological Review, 95, 411-33.
82. Kintsch, W. (1970). Learning, Memory, and Conceptual Processes. New York, NY: John Wiley & Sons.
83. Kruschke, J. K. (1992). ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review, 99, 22-44.
84. Kruschke, J. K. (1993). Human category learning: Implications for backpropagation models. Connection Science, 5, 3-36.
85. Leighton, J. P. & Dawson, M. R. W. (2001). A parallel distributed processing model of Wason's selection task. Cognitive Systems Research, 2, 207-31.
86. Lettvin, J. Y., Maturana, H. R., McCulloch, W. S., & Pitts, W. H. (1959). What the frog's eye tells the frog's brain. Proceedings of the IRE, 47(11), 1940-51.
87. Levitan, I. B. & Kaczmarek, L. K. (1991). The Neuron: Cell and Molecular Biology. New York: Oxford University Press.
88. Lewis, C. I. & Langford, C. H. (1959). Symbolic logic, 2nd edn. New York: The Century Co.
89. Lippmann, R. P. (1987). An introduction to computing with neural nets. IEEE ASSP Magazine, April, 4-22.
90. Lippmann, R. P. (1989). Pattern classification using neural networks. IEEE Communications Magazine, November, 47-64.
91. Long, J. M., Mellem, J. E., & Kesner, R. P. (1998). The effects of parietal cortex lesions on an object spatial location paired-associate task in rats. Psychobiology, 26(2), 128-33.
92. Martinez, J. L. & Derrick, B. E. (1996). Long-term potentiation and learning. Annual Review of Psychology, 47, 173-203.
93. McCaughan, D. B., Medler, D. A., & Dawson, M. R. W. (1999). Internal representation in networks of nonmonotonic processing units. Paper presented at the International Joint Conference on Neural Networks, Washington, DC.
94. McClelland, J. L. (1986). Resource requirements of standard and programmable nets. In D. Rumelhart, J. McClelland and the PDP Group (eds), Parallel Distributed Processing (vol. 1, pp. 460-87). Cambridge, MA: MIT Press.
95. McClelland, J. L. & Rumelhart, D. E. (1986). Parallel Distributed Processing, vol. 2. Cambridge, MA: MIT Press.
96. McClelland, J. L. & Rumelhart, D. E. (1988). Explorations In Parallel Distributed Processing. Cambridge, MA: MIT Press.
97. McCloskey, M. (1991). Networks and theories: The place of connectionism in cognitive science. Psychological Science, 2, 387-95.
98. McCulloch, W. S. (1988). Agathy tyche of nervous nets - the lucky reckoners. In W. S. McCulloch (ed.), Embodiments of Mind. Cambridge, MA: MIT Press.
99. McCulloch, W. S. & Pitts, W. H. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115-33.
100. McCulloch, W. S. & Pitts, W. H. (1988). A logical calculus of the ideas immanent in nervous activity. In W. S. McCulloch (ed.), Embodiments Of Mind (pp. 19-39). Cambridge, MA: MIT Press.
101. Medler, D. A. (1998). A brief history of connectionism. Neural Computing Surveys, 1, 18-72.
102. Medler, D. A., McCaughan, D. B., Dawson, M. R. W., & Willson, L. (1999). When local isn't enough:Extracting distributed rules from networks. Paper presented at the International Joint Conference On Neural Networks, Washington, D.C.
103. Mendelson, E. (1970). Schaum's outline of theory and problems of Boolean algebra and switching circuits. New York: McGraw-Hill.
104. Minsky, M. (1963). Steps toward artificial intelligence. In E. A. Feigenbaum & J. Feldman (eds), Computers and Thought (pp. 406-50). New York, NY: McGraw-Hill.
105. Minsky, M. & Papert, S. (1988). Perceptrons, 3rd edn. Cambridge, MA: MIT Press.
106. Moody, J. (1998). Forecasting the economy with neural nets: A survey of challenges and solutions. In G. Orr & K. Muller (eds), Neural Networks: tricks of the trade (pp. 347-72). Berlin; New York: Springer.
107. Moorhead, I. R., Haig, N. D., & Clement, R. A. (1989). An investigation of trained neural networks from a neurophysiological perspective. Perception, 18, 793-803.
108. Mozer, M. C. & Smolensky, P. (1989). Using relevance to reduce network size automatically. Connection Science, 1, 3-16.
109. Murdock, B. B. (1982). A theory for the storage and retrieval of item and associative information. Psychological Review, 89, 609-26.
110. Murdock, B. B. (1997). Context and mediators in a theory of distributed associative memory (TODAM2). Psychological Review, 104, 839-62.
111. Neuneier, R. & Zimmermann, H. G. (1998). How to train neural networks. In G. Orr & K. Muller (eds), Neural Networks: tricks of the trade (pp. 373-423). Berlin; New York: Springer.
112. O'Keefe, J. & Dostrovsky, J. (1971). The hippocampus as a spatial map: preliminary evidence from unit activity in the freely moving rat. Brain Research, 34, 171-5.
113. O'Keefe, J. & Nadel, L. (1978). The Hippocampus As A Cognitive Map. Oxford: Clarendon Press.
114. Omlin, C. W. & Giles, C. L. (1996). Extraction of rules from discrete-time recurrent neural networks. Neural Networks, 9, 41-52.
115. Orr, G. & Muller, K. (1998). Neural Networks: tricks of the trade. Berlin; New York: Springer.
116. Pao, Y.-H. (1989). Adaptive Pattern Recognition and Neural Networks. Reading, MA: Addison-Wesley.
117. Papert, S. (1988). One Ai or Many? Daedalus, 117(1), 1-14.
118. Pearce, J. M. (1997). Animal Learning and Cognition: An Introduction. East Sussex: Psychology Press.
119. Pearce, J. M. & Bouton, M. E. (2001). Theories of associative learning in animals. Annual Review of Psychology, 52, 111-39.
120. Pfeifer, R. & Scheier, C. (1999). Understanding Intelligence. Cambridge, MA: MIT Press.
121. Piattelli-Palmarini, M. (1989). Evolution, selection and cognition: From "learning" to parameter setting in biology and in the study of language. Cognition, 31, 1-44.
122. Piercey, C. D. & Dawson, M. R. W. (1999). Coarse coding in value units: Subsymbolic implications of nonmonotonic processing units. In M. Hahn & S. C. Stoness (eds), Proceedings of the Twenty First Annual Conference of the Cognitive Science Society (pp. 537-42). Malwah, NJ: Lawrence Erlbaum Associates.
123. Pike, R. (1984). Comparison of convolution and matrix distributed memory systems for associative recall and recognition. Psychological Review, 91, 281-94.
124. Pinker, S. (2002). The Blank Slate. New York, NY: Viking.
125. Poggio, T. & Girosi, F. (1990). Regularization algorithms for learning that are equivalent to multilayer networks. Science, 247, 978-82.
126. Poggio, T., Torre, V., & Koch, C. (1985). Computational vision and regularization theory. Nature, 317, 314-19.
127. Poldrack, R. A., Clark, J., Pare-Blagoev, E. J., Shohamy, D., Moyano, J. C., Myers, C., & Gluck, M. A. (2001). Interactive memory systems in the human brain. Nature, 414(6863), 546-50.
128. Pylyshyn, Z. W. (1991). The role of cognitive architectures in theories of cognition. In K. VanLehn (ed.), Architectures For Intelligence (pp. 189-223). Hillsdale, NJ: Lawrence Erlbaum Associates.
129. Renals, S. (1989). Radial basis function network for speech pattern classification. Electronics Letters, 25, 437-9.
130. Ripley, B. D. (1996). Pattern Recognition and Neural Networks. Cambridge, UK: Cambridge University Press.
131. Rizzuto, D. S. & Kahana, M. J. (2001). An autoassociative neural network model of paired-associate learning. Neural Computation, 13(9), 2075-92.
132. Rojas, R. (1996). Neural Networks: A Systematic Exploration. Berlin: Springer.
133. Rosenblatt, F. (1962). Principles of Neurodynamics. Washington: Spartan Books.
134. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986a). Learning internal representations by backpropagating errors. Nature, 323, 533-6.
135. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986b). Learning internal representations by error backpropagation. In D. Rumelhart, J. McClelland and the PDP Group (eds), Parallel Distributed Processing (vol. 1. pp. 318-62) Cambridge, MA: MIT Press.
136. Rumelhart, D. E. & McClelland, J. L. (1986). Parallel Distributed Processing, vol. 1. Cambridge, MA: MIT Press.
137. Seidenberg, M. (1993). Connectionist models and cognitive theory. Psychological Science, 4, 228-35.
138. Seidenberg, M. & McClelland, J. (1989). A distributed, developmental model of word recognition and naming. Psychological Review, 97, 447-52.
139. Selfridge, O. G. (1956). Pattern recognition and learning. In C. Cherry (ed.), Information Theory (pp. 345-53). London: Butterworth Scientific Publications.
140. Shanks, D. R. (1995). The Psychology of Associative Learning. Cambridge, UK: Cambridge University Press.
141. Shannon, C. E. (1938). A symbolic analysis of relay and switching circuits. Transactions of the American Institute of Electrical Engineers, 57, 713-23.
142. Shannon, C. E. (1949). The synthesis of two-terminal switching circuits. Bell System Technical Journal, 28, 59-98.
143. Shepherd, A. J. (1997). Second-Order Methods For Neural Networks. London: Springer.
144. Smolensky, P. (1988). On the proper treatment of connectionism. Behavioral and Brain Sciences, 11, 1-74.
145. Sporns, O. & Tononi, G. (1994). Selectionism and the Brain. San Diego, CA: Academic Press.
146. Steinbuch, K. (1961). Die lernmatrix. Kybernetik, 1, 36-45.
147. Sutton, R. S. & Barto, A. G. (1981). Toward a modern theory of adaptive networks: Expectation and prediction. Psychological Review, 88(2), 135-70.
148. Sutton, R. S. & Barto, A. G. (1998). Reinforcement Learning. Cambridge, MA: MIT Press.
149. Taylor, W. K. (1956). Electrical simulation of some nervous system functional activities. In C. Cherry (ed.), Information Theory (pp. 314-28). London: Butterworth Scientific Publications.
150. Tesauro, G. & Janssens, B. (1988). Scaling relationships in backpropagation learning. Complex Systems, 2, 39-44.
151. Thrun, S. B., Bala, J., Bloedorn, E., Bratko, I., Cestnik, B., Cheng, J., De Jong, K., Dzeroski, S., Fahlman, S. E., Fisher, D., Hamann, R., Kaufman, K., Keller, S., Kononenko, I., Kreuziger, J., Michalski, R. S., Mitchell, T., Pachowicz, P., Reich, Y., Vafaie, H., Van de Welde, W., Wenzel, W., Wnek, J., & Zhang, J. (1991). The MONK's Problems - A Performance Comparison of Different Learning Algorithms (Technical Report CS-CMU-91-197). Pittsburgh: Carnegie Mellon University.
152. Underwood, B. J. (1957). Interference and forgetting. Psychological Review, 64(1), 49-60.
153. Underwood, B. J. (1966). Experimental Psychology, 2nd edn. New York, NY: Appleton-Century-Crofts.
154. Underwood, B. J., Runquist, R. N. & Schulz, R. W. (1959). Response learning in paired-associate lists as a function of intralist similarity. Journal of Experimental Psychology, 58(1), 70-78.
155. Widrow, B. & Hoff, M. E. (1960). Adaptive switching circuits. Institute of Radio Engineers, Wester Electronic Show and Convention, Convention Record, Part 4, 96-104.
156. Wimer, C. C. & Lambert, W. E. (1959). The differential effects of word and object stimuli on the learning of paired associates. Journal of Experimental Psychology, 57(1), 31-6.
157. Zurada, J. M. (1992). Introduction To Artificial Neural Systems. St. Paul, MN: West.