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Realistic neural nets need to learn iconic representations

Published online by Cambridge University Press:  19 May 2011

W. A. Phillips ,
P. J. B. Hancock  and
L S. Smith
W. A. Phillips
Affiliation:
Centre for Cognitive and Computational Neuroscience, Departments of Psychology and Computing Science, Stirling University, Stirling FK9 4LA, Scotland, Electronic mail: [email protected]
P. J. B. Hancock
Affiliation:
Centre for Cognitive and Computational Neuroscience, Departments of Psychology and Computing Science, Stirling University, Stirling FK9 4LA, Scotland, Electronic mail: [email protected]
L S. Smith
Affiliation:
Centre for Cognitive and Computational Neuroscience, Departments of Psychology and Computing Science, Stirling University, Stirling FK9 4LA, Scotland, Electronic mail: [email protected]
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Information
Behavioral and Brain Sciences , Volume 13 , Issue 3 , September 1990 , pp. 505
Copyright
Copyright © Cambridge University Press 1990

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References

Ackley, D., Hinton, G. E. & Sejnowski, T. J. (1985) A learning algorithm for Boltzmann machines. Cognitive Science 9(1): 147–69. {aSJH, GDAB}Google Scholar
Albus, J. S. (1975) A new approach to manipulator control: The cerebellar model articulation controller (CMAC). American Society of Engineers, Transactions G (Journal of Dynamic Systems, Measurement and Control) 97(3): 220–27. {aSJH}Google Scholar
Allen, R. B. (1990) Connectionist language users. Bellcore Technical Report. Morristown, NJ. {rSJH}Google Scholar
Amit, D. J. (1989) Modeling brain function. Cambridge University Press. {GT}Google Scholar
Andersen, R. A. (1986) Value, variable, and coarse coding by posterior parietal neuronns. Behavioral and Brain Sciences 9:90–91. {SB}Google Scholar
Andersen, R. A., Asanuma, C. & Cowan, W. M. (1985) Callosal and prefrontal associational-projecting cell populations in area 7a of the macaque monkey: A study using retrogradely transported fluorescent dyes. Journal of Comparative Neurology 232:443–55. {SB}Google Scholar
Andersen, R. A., Essick, G. K. & Siegel, R. M. (1987) Neurons of area 7 activated by both visual stimuli and oculomotor behavior. Experimental Brain Research 67:316–22. {SB}Google Scholar
Anderson, J. A., Silverstein, J. W. & Ritz, S. R., Jones, R. S. (1977) Distinctive features, categorical perception, and probability learning: Some applications of a neural model. Psychological Review 84:413–451. {aSJH}Google Scholar
Anderson, J. R. (1983) The architecture of cognition. Harvard University Press. {aSJH}Google Scholar
Atrim, J. & Bridgeman, B. (1989) The physiology of attention: Participation of cat striate cortex in behavioral choice. Psychological Research 50:223–28. {BB}Google Scholar
Barash, S., Bracewell, R. M., Fogassi, L. & Andersen, R. A. (1989) Interactions of visual and motor-planning activities in the lateral intra-parietal area (LIP). Society for Neuroscience Abstracts 15:1203. {SB}Google Scholar
Barron, A. R., & Barron, R. L. (1988) Statistical learning networks: A unifying view. Symposium on the interface: Statistics and computing science. April 21–23, Reston, VA. {RMG}Google Scholar
Baum, E. B. (1989) A proposal for more powerful learning algorithms. Neural Computation 1(2):201–07. {rSJH}CrossRefGoogle Scholar
Baum, E. B., & Haussler, D. (1988) What size net gives valid generalization? In: Advances in neural information processing systems 1, ed. Touretzky, D.. Morgan-Kaufman. {arSJH}Google Scholar
Baum, E. B., & Wilczek, F. (1987) Supervised learning of probability distributions by neural networks. In: Neural information processing systems, ed. Anderson, D.. American Institute of Physics. {aSJH}Google Scholar
Blum, A. & Rivest, R. (1989) Training a 3-node neural network is NP-complete. In: Advances in neural information processing systems 1, ed. Touretzky, D.. Morgan-Kaufman. {arSJH}Google Scholar
Bower, G. H., & Hilgard, E. R. (1981) Theories of learning, 5th ed.Prentice Hall. {JKK}Google Scholar
Braitenberg, V. (1984) Vehicles: Experiments in synthetic psychology. MIT Press. {MW}Google Scholar
(1990) Reading the structure of brains. Network 1:1–11. {GT}CrossRefGoogle Scholar
Branch, M. N. (1982) Misrepresenting behaviorism. Behavioral and Brain Sciences 5:372–73. {WSM}Google Scholar
Bridgeman, B. (1980) Temporal response characteristics of cells in monkey striate cortex measured with metacontrast masking and brightness discrimination. Brain Research 196:347–64. {BB}Google Scholar
(1982) Multiplexing in single cells of the alert monkey’s visual cortex during brightness discrimination. Neuropsychologia 20:33–42. {BB}Google Scholar
(in press) Review of C. Koch & I. Segev (eds.),Methods in neuronal modeling. Science Books and Films. {BB}Google Scholar
Brown, G. D. A., & Oaksford, M. (1990a) Symbolic behaviour and code generation: The emergence of “equivalence relations” in neural networks. In: Cybernetics and systems ’90, ed. Trappl, R.. World Scientific Publishing Corp. {GDAB}Google Scholar
(1990b) The development of “symbolic behaviour” in natural and artificial neural networks. In: Parallel processing in neural systems and computers, ed. Eckmiller, R., Hartmann, G. & Hauske, G.. Elsevier Science Publishers. {GDAB}Google Scholar
Burr, D. J. (1988) Experiments on neural net recognition of spoken and written text. IEEE Transactions on Acoustics, Speech and Signal Processing 36(7). {aSJH}Google Scholar
Carroll, S. M., & Dickenson, B. (1989) Construction of neural nets using the radon transform. Proceedings of the International Joint Conference on Neural Networks. {aSJH}Google Scholar
Chandrasekaran, B., Goel, A. & Allemang, D. (1988) Information processing abstractions: The message still counts more than the medium. Behavioral and Brain Sciences 11:26–27. {KH}Google Scholar
Chater, N. & Oaksford, M. (1990) Autonomy, implementation and cognitive architecture: A reply to Fodor and Pylyshyn. Cognition 34:93–107. {NC}Google Scholar
Chauvin, Y. (1989) A back propagation network with optimal use of hidden units. In: Advances in neural information processing, ed. Touretzky, D., Systems 1. Morgan-Kaufman. {aSJH}Google Scholar
Chomsky, N. (1959) A review of B. F. Skinner’s Verbal behavior. Language 35:26–58. {aSJH}Google Scholar
(1980) Rules and representations. Columbia University Press. {aSJH}Google Scholar
(1986) Knowledge of language: Its nature, origin and uses. Praeger. {JER}Google Scholar
Churchland, P. S., & Sejnowski, T. J. (1989) Neural representation and neural computation. In: Neural connections, mental computation, ed. Nadel, L., Cooper, L., Culicover, P. & Harnish, M.. Bradford Books/MIT Press. {NES}Google Scholar
Cooper, P. (1962) The hypersphere in pattern recognition. Information and Control 5:324–46. {aSJH}Google Scholar
Cover, T. M. (1965) Geometrical and Statistical Properties of linear inequalities with applications to pattern recognition. IEEE Transactions on Electronic Computers, vol. EC-14, 3. {aSJH}Google Scholar
Crick, F. C. (1989) Neural Edelmanism. Trend in Neuroscience 12:240–48. {GT}Google Scholar
Cybenko, G. (in press) Approximation by superposition of a sigmoidal function. Mathematical Control Systems Signals. {aSJH}Google Scholar
Derthick, M. (1987) A connectionist architecture for representing and reasoning about structured knowledge. Research Report No. CMU-BOLTZ-29. Department of Computer Science, Carnegie-Mellon University. {NC}Google Scholar
Dietterich, T. G., & Shavlik, J. W., eds. (in press) Readings in machine learning. Morgan Kaufmann. {PL}Google Scholar
Donahoe, J. W., & Palmer, D. C. (1989) The interpretation of complex human behavior: Some reactions to parallel distributed processing. Journal of the Experimental Analysis of Behavior 51:399–416. {WSM}CrossRefGoogle Scholar
Duda, R. O., & Hart, P. E. (1973) Pattern classification and scene analysis. J. Wiley. {aSJH}Google Scholar
Dugdale, N. & Lowe, C. F. (1990) Naming and stimulus equivalence. In: Behaviour analysis in theory and practice: Contributions and controversies, ed. Blackman, D. E. & Lejeune, H.. Lawrence Erlbaum Associates. {GDAB}Google Scholar
Dunn-Rankin, (1983) Scaling methods. Erlbaum. {aSJH}Google Scholar
Durbin, R. & Rumelhart, D. E. (1989) Product units: A computationally powerful and biologically plausible extension to backpropagation networks. Neural Computation 1:133–42. {aSJH}Google Scholar
Edelman, G. (1987) Neural Darwinism: The theory of neuronal group selection. Basic Books. {MW}Google Scholar
Elman, J. (1988) Finding structure in time. Center for Research on Language Technical Report 8801. Center for Research in Language, University of California, San Diego, CA. {arSJH}Google Scholar
(1989) Representation and structure in connectionist models. Technical Report 8903. Center for Research in Language, University of California, San Diego, CA. {MW}Google Scholar
Epstein, R. (1982) Representation: A concept that fills no gaps. Behavioral and Brain Sciences 5:377–78. {WSM}Google Scholar
Estes, W. K. (1959) Toward a statistical theory of learning. Psychology Review 57:94–107. {rSJH}Google Scholar
Estes, W. K., Campbell, J. A., Hatsopoulos, N. & Hurwitz, J. B. (1988) Base-rate effects in category learning: A comparison of parallel network and memory storage-retrieval models. Journal of Experimental Psychology, Learning, Memory ir Cognition 15:556–76. {JKK}Google Scholar
Everitt, B. (1975) Cluster analysis. Heinemann Educational Books. {aSJH}Google Scholar
Feldman, J. A. (1985) Connectionist models and their applications. Cognitive Science, Special Issue 9:1. {aSJH}Google Scholar
Feldman, J. A., & Ballard, D. H. (1982) Connectionist models and their properties. Cognitive Science 6:205–54. {JER}Google Scholar
Fisher, D. H., & McKusick, K. B. (1989) An empirical comparison of ID3 and back-propagation. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, Detroit, MI. Morgan Kaufmann. {PL}Google Scholar
Fodor, J. (1988) Personal communication, Princeton. {aSJH, JH}Google Scholar
Fodor, J. & Pylyshym, Z. (1988) Connectionism and cognitive architecture: A critical analysis. In: Connections and symbols, ed. Pinker, S. & Meher, J.. MIT Press. {aSJH, JH, GDAB, NC, PL, TVG}Google Scholar
Francis, W. N., & Kucera, H. (1979) Manual of information to accompany a standard corpus of present-day edited American English for use with digital computers. Department of Linguistics, Brown University. {aSJH}Google Scholar
Fujimura, O. (1987) Fundamentals and applications in speech production research. Proceedings of the XIth Congress on Phonetic Science 6:10–27. {MIJ}Google Scholar
Gardner, H. (1985) The mind’s new science. Basic Books. {aSJH}Google Scholar
Gibson, J. J. (1966) The senses considered as perceptual systems. Houghton Mifflin. {WAP}Google Scholar
Giles, C, Griffin, R. D. & Maxwell, T. (1988) Encoding geometric in variances in higher order neural networks. In: Neural information processing - natural and synthetic, ed. Anderson, D.. American Institute of Physics. {aSJH}Google Scholar
Gluck, M. A., & Bower, G. H. (1986) Conditioning and categorization: Some common effects of informational variables in animal and human learning. Cognitive science meeting, Amherst, MA. {aSJH}Google Scholar
(1988) A configural-cue network model of classification learning. Presented at the Psychonomic Society Annual Conference, Chicago. {KH}Google Scholar
(1988) Evaluating an adaptive network model of human learning. Journal of Memory and Language 27:166–95. {JKK}CrossRefGoogle Scholar
Gluck, M. A., & Chow, W. (1989) Dynamic stimulus-specific learning rates and the representation of dimensionalized stimulus structures. Paper submitted to the IEEE Conference on Neural Information Processing Systems, November 27–30, Denver, CO. {JKK}Google Scholar
Gold, E. M. (1967) Language identification in the limit. Information and Control 16:447–74. {rSJH}Google Scholar
Golden, R. (1988a) A unified framework for connectionist systems. Biological Cybernetics 59:109–20. {aSJH, RMG}Google Scholar
(1988b) Statistical rationality. Behavioral and Brain Sciences 11:35. {RMG}CrossRefGoogle Scholar
Gorman, P. R., & Sejnowski, T. (1988) Learned classification of sonar targets using a massively parallel network. IEEE Transactions on Acoustics Speech and Signal Processing 36:1135–40. {aSJH, JER}Google Scholar
Grossberg, S. (1976) Adaptive pattern classification and universal recoding I. Parallel development and coding of neural feature detectors. Biological Cybernetics 23:121–34. {aSJH}Google Scholar
(1987) Competitive learning: From interactive activation to adaptive resonance. Cognitive Science 11(1):23–64. {aSJH, JKK, MW}Google Scholar
Hancock, P. J. B. (1989) Data representation in neural nets: An empirical study. In: Proceedings of the connectionist models summer school, ed. Touretzky, D., Hinton, G. & Sejnowski, T.. Morgan Kaufmann. {WAP}Google Scholar
Hanson, S. J. (in press) The stochastic delta rule. Physica D. {aSJH}Google Scholar
Hanson, S. J., & Bauer, M. (1986) Conceptual clustering, machine learning and polymorphy. In: Uncertainty in artificial intelligence, ed. Kanal, L. & Lemmer, J.. Amsterdam. {aSJH}Google Scholar
(1989) Conceptual clustering, categorization, and polymorphy. Machine Learning 3:343–72. {aSJH}Google Scholar
Hanson, S. J., & Burr, D. J. (1987a) Knowledge representation in connectionist networks. Unpublished manuscript. {aSJH}Google Scholar
(1987b) Minkowski-r Backpropagation: Learning in connectionist models with non-Euclidean error signals. In: Proceedings of neural networks: Natural and synthetic, ed. Anderson, D.. American Institute of Physics. {aSJH}Google Scholar
Hanson, S. J., & Kegl, J. (1987) PARSNIP: A connectionist model that learns natural language grammar from exposure to natural language sentences. Ninth Annual Cognitive Science Conference, Seattle, WA. {aSJH, RMG}Google Scholar
Hanson, S. J., & Pratt, L. (1989) Some comparisons of constraints for back-propagation networks. In: Advances in neural information processing, ed. Touretzky, D.. Morgan-Kaufmann. {aSJH}Google Scholar
Hanson, S. J., & Olson, C. R. (1988) A connectionist network that computes limb position in a head-centered coordinate frame. Society for Neuroscience Abstracts, Neurosciences Conference, Toronto, Ontario. {aSJH}Google Scholar
Harnad, S. (in press) The symbol grounding problem. Physica D. {aSJH, JH}Google Scholar
Hartigan, J. (1969) Clustering algorithms. Wiley & Sons. {aSJH}Google Scholar
Hebb, D. O. (1949) Organization of behavior: A neuropsychological theory. Wiley. {aSJH, WAP, MW}Google Scholar
Hendler, J. (1989a) Marker-passing over microfeatures: Towards a hybrid symbolic/connectionist model. Cognitive Science 13(1):79–106. {JH}Google Scholar
(1989b) Spreading activation over distributed microfeatures. In: Advances in neural information processing systems 1, ed. Touretzky, D. S.. Morgan-Kaufman. (Collected papers of the IEEE Conference on Neural Information Processing Systems — Natural and Synthetic, Denver 1988.) {JH}Google Scholar
(submitted) Spreading actuation in PDP networks. {JH}Google Scholar
Hinton, G. E. (1981) Implementing semantic networks in parallel hardware. In: Parallel models of associative memory, ed. Hinton, G. E. & Anderson, J. A.. Lawrence Erlbaum Associates. {NC}Google Scholar
(1987a) Learning procedures for connectionist models. Carnegie Mellon University technical report. {aSJH}Google Scholar
(1987b) Learning distributed representations of concepts. In: Eighth Annual Conference of the Cognitive Science Society. Lawrence Erlbaum. {aSJH, NC}Google Scholar
Hinton, G. E., McClelland, J. L. & Rumelhart, D. E. (1986) Distributed representations. In: Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1, ed. McClelland, J. L. & Rumelhart, D. E.. MIT Press. {KH}Google Scholar
Hornik, K., Stinchcombe, M. & White, H. (1988) Multi-layer feedforward networks are universal approximators. Unpublished manuscript. {aSJH}CrossRefGoogle Scholar
Hyvarinen, J. & Poranen, A. (1974) Function of the posterior-associative area 7 as revealed from cellular discharge in alert monkeys. Brain 97:673–92. {SB}Google Scholar
Iba, W. & Langley, P. (1987) A computational theory of motor learning. Computational Intelligence 3:338–50. {PL}Google Scholar
Jacobs, R. A., Jordan, M. I., & Barto, A. G. (1990) Task decomposition through competition in a modular connectionist architecture: The what and where vision tasks. Cognitive Science {MIJ}Google Scholar
Jones, E. G. (1985) The thalamus. Plenum Press. {aSJH}Google Scholar
Jordan, M. I. (1986a) Serial order: A parallel processing approach. Center for Human Information Processing, University of California at San Diego technical report. {aSJH}Google Scholar
(1986b) Attractor dynamics and parallelism in a connectionist sequential machine. In: Proceedings of the 1986 Cognitive Science Conference. Lawrence Erlbaum. {rSJH}Google Scholar
(1990) Motor learning and the degrees of freedom problem. In: Attention and performance XIII, ed. Jeannerod, M.. Erlbaum. {MIJ}Google Scholar
Judd, J. S. (1987) Learning in networks is hard. In: Proceedings of the First International Conference on Neural Networks. IEEE, June, San Diego, CA. {aSJH}Google Scholar
(1988) On the complexity of loading shallow neural networks. Journal of Complexity 4(3): 177–92. {rSJH}Google Scholar
Kaplan, S. & Kaplan, R. (1982) Cognition and environment. Praeger. {MW}Google Scholar
Kaplan, S., Weaver, M. & French, R. M. (1990) Active symbols and internal models: Towards a cognitive connectionism. AI & Society 4(1). {MW}Google Scholar
Kehoe, E. J. (1988) A layered network model of associative learning: Learning to learn and configuration. Psychological Review 95:411—33. {WSM}Google Scholar
(1989) Connectionist models of conditioning: A tutorial. Journal of the Experimental Analysis of Behavior 52:427–40. {WSM}CrossRefGoogle Scholar
Kehoe, E. J., Schreuers, B. G. & Graham, P. (1987) Temporal primacy overrides prior training in serial compound conditioning of the rabbit’s nictitating membran eresponse. Animal Learning & Behavior 15:455–64. {WSM}Google Scholar
Klahr, D., Langley, P. & Neches, R., eds. (1987) Production system models of learning and development. MIT Press. {PL}Google Scholar
Koch, C. & Segev, I. (1989) Methods in neuronal modeling. Bradford Books/MIT Press. {BB}Google Scholar
Koford, K. A. (1962) Adaptive network organization. Stanford Electronics Laboratory Quarterly Research Review, No. 3. {aSJH}Google Scholar
Kohonen, T. (1977) Associative memory: A system-theoretical approach. Springer-Verlag. {aSJH}Google Scholar
(1988) Self-organization and associative memory. Springer-Verlag. {WAP}Google Scholar
Kohler, W. (1925) The mentality of apes (trans, by Winter, E.). Harcourt, Brace & World. {NES}Google Scholar
Krebs, J. (1978) Optimal foraging: Decision rules for predators. In: Behavioral ecology: An evolutionary approach, ed. Krebs, J. R. & Davies, N. B.. Sinauer. {aSJH}Google Scholar
Kruskal, J. & Shepard, R. (1974) A nonmetric variety of linear factor analysis. Psychometrika 39:2. {aSJH}Google Scholar
Kruschke, J. K. (in progress) A connectionist model of category learning. Ph.D. dissertation, University of California at Berkeley. {JKK}Google Scholar
Laird, J. E., Rosenbloom, P. S. & Newell, A. (1985) Towards chunking as a general learning mechanism. Technical Report CMU-CS-85–110. Department of Computer Science, Carnegie Mellon University {aSJH}Google Scholar
Langley, P. (1983) Representational issues in learning systems. Computer 16(9): 47–51. {aSJH}Google Scholar
Lachter, J. & Bever, T. G. (1988) The relations between linguistic structure and associative theories of language learning: A constructive critique of some connectionist learning models. Cognition 28:195–247. {JKK}Google Scholar
Laird, J., Yager, E. S., Tuck, C. M. & Hucka, M. (1989) Learning in teleautonomous systems using SOAR. Proceedings of the 1989 NASA Conference on Space Telerobotics. {PL}Google Scholar
LeCun, Y. (1989) Generalization and network design strategies. In: Connectionism in perspective, ed. Pfeifer, R., Schreter, Z., Fogelman, F. & Steels, L.. Elsevier. {MIJ}Google Scholar
Langley, P. (1983) Representational issues in learning systems. IEEE Computer 16:47–51. {PL}Google Scholar
(1989) Toward a unified science of machine learning. Machine Learning 3:253–59. {PL}Google Scholar
Lehky, S. R., & Sejnowski, T. J. (1988) Network model of shape-from-shading: Neural function arises from both receptive and projective fields. Nature 333:452–54. {JKK}Google Scholar
Lenat, D. (1985) The role of heuristics in learning by discovery: Three case studies. In: Machine learning, ed. Michalski, R. S., Carbonell, J. G., & Mitchell, T. M.. Tioga Publishing. {aSJH}Google Scholar
Lenat, D. B., & Brown, J. S. (1984) Why AM and EURISKO appear to work. Artificial Intelligence 23:269–94. {KL}Google Scholar
Levelt, W. J. M. (1974) Forman grammars in linguistics and psycholinguistics, 3 vols. Mouton. {WJML}Google Scholar
Lightfoot, D. (1989) The child’s trigger experience: Degree-0: Learnability. Behavioral and Brain Sciences 12(2): 321–75. {aSJH}Google Scholar
Logue, A. W. (1982) Cognitive psychology’s representation of behaviorism. Behavioral and Brain Sciences 5:381–82. {WSM}Google Scholar
Longuet-Higgins, C, Willshaw, D. J. & Buneman, O. P. (1970) Theories of associative recall. Quarterly Review of Biophysics 3(2): 223–44. {aSJH}Google Scholar
Luce, D. & Krumhansl, C. (1990) Measurement, scaling and psychophysics. In: Steven’s handbook of experimental psychology, ed. Atkinson, R. C., Lindzay, D., Herrnstein, R. J. & Luce, D.. Wiley Interscience. {aSJH}Google Scholar
Lynch, J. C, Mountcastle, V. B., Talbot, W. H. & Yin, T. C. T. (1977) Parietal lobe mechanisms for directed visual attention. Journal of Neurophysiology 40:362–89. {SB}CrossRefGoogle ScholarPubMed
Maddieson, I. (1984) Patterns of sound. Cambridge University Press. {MIJ}Google Scholar
Maki, W. S., & Abunawass, A. (in press) A connectionist approach to conditional discriminations: Learning, short-term, and attention. Presented at the 12th Symposium on Models of Behavior, Harvard University, June 1989. In: Neural network models of conditioning and action, ed. M. Commons, S. Grossberg, & J. Staddon. {WSM}Google Scholar
Marr, D. (1982) Vision. Freeman. {aSJH }Google Scholar
Massaro, D. W. (1988) Some criticisms of connectionist models of human performance. Journal of Memory and Language 27:213–34. {KH, KL}Google Scholar
McClelland, J. L. (1986) The programmable blackboard model of reading. In: Parallel distributed processing: Explorations in the microstructure of cognition, vol. 2: Psychological and biological models, ed. McClelland, J. L. & Rumelhart, D. E.. MIT Press. {JER}Google Scholar
McClelland, J. L., & Rumelhart, D. E. (1981) An interactive activation model of context effects in letter perception, Part 1: An account of the basic findings. Psychological Review 88:375–407. {KH, WSM}Google Scholar
(1985) Distributed memory and the representation of general and specific information. Journal of Experimental Psychology: General 114:159–88. {KH}CrossRefGoogle Scholar
(1986) On learning the past tenses of English verbs. In: Parallel distributed processing, vol. 2, ed. Rumelhart, D. E. & McClelland, J. L.. MIT Press. {JKK}Google Scholar
(1988) Explorations in parallel distributed processing: A handbook of models, programs, and exercises. MIT Press. {KH}Google Scholar
McCloskey, M. & Cohen, N. J. (1987) The sequential learning problem in connectionist modeling. Paper prepared for presentation at the annual meeting of the Psychonomic Society, November, Seattle, WA. {JKK}Google Scholar
McCulloch, W. S., & Pitts, W. (1943) A logical calculus of the ideas imminent in nervous activity. Bulletin of Mathematical Biophysics 5:115–33. {arSJH}Google Scholar
Mezard, M. & Nadal, J. P. (1989) Learning in feedforward layered networks: The tiling algorithm. Journal of Physiology A: Mathematics General 22:2191–203. {GT}Google Scholar
Miller, G. A. (1987) Personal communication. {aSJH}Google Scholar
Minsky, M. (1961) Steps towards artificial intelligence. Proceedings of the Institute of Radio Engineers 49:8–30. {aSJH}Google Scholar
(1967) Computation: Finite and infinite machines. Prentice Hall. {NC}Google Scholar
Minsky, M. & Papert, S. (1969) The perceptron: Principles of computational geometry. MIT Press. {aSJH}Google Scholar
(1969) Perceptrons. MIT Press. {KH}Google Scholar
Minton, S. (1988) Quantitative results concerning the utility of explanation-based learning. Proceedings of the Seventh National Conference on Artificial Intelligence, St. Paul, MN. Morgan Kaufmann. {PL}Google Scholar
Miyata, Y. (1988) The learning and planning of actions. Ph.D. thesis, Psychology Department, University of California at San Diego. Technical Report 8802, Institute for Cognitive Science. {rSJH}Google Scholar
Mooney, R., Shavlik, S., Towell, G. & Gove, A. (1989) An experimental comparison of symbolic and connectionist learning algorithms. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, Detroit, MLMorgan Kaufmann. {PL}Google Scholar
Mozer, M. & Smolensky, P. (1989) Skeletonization: A technique for trimming the fat from a network via relevance assessment. In: Advances in neural information processing systems, ed. Touretzky, D.. Morgan Kaufman. {aSJH}Google Scholar
Murdock, B. B. (1979) Convolution and correlation in perception and memory. In: Perspectives on memory research: Essays in honor of Uppsala University’s 500th anniversary, ed. L.-G. Nilsson. {TVG}Google Scholar
Nadel, L. (1982) Some thoughts on the proper foundations for the study of cognition in animals. Behavioral and Brain Sciences 5:383–84. {WSM}Google Scholar
Natarajan, B. K. (1987) On learning Boolean functions. Proceedings of the Nineteenth Annual Association for Computing Machinery Symposium on Theory of Computing. {rSJH}Google Scholar
Newell, A. & Simon, H. (1976) Computer science as an empirical inquiry. Communications of the Association for Computing Machinery 19:113—26. {TVG}Google Scholar
Nilsson, N. J. (1965) Learning machines: Foundations of trainable pattern-classifying systems. McGraw-Hill. {aSJH}Google Scholar
Norman, D. A. (1986) Reflections on cognition and parallel distributed processing. In: Parallel distributed processing: Explorations in the microstructure of cognition, vol. 2, ed. McClelland, J. L. & Rumelhart, D. E.. MIT Press. {KH}Google Scholar
Palm, G. (1982) Neural assemblies: An alternative approach to artificial intelligence. Springer-Verlag. {MW}Google Scholar
Pavel, M. (1988) Personal communication, Boston, MA. {aSJH}Google Scholar
Pearlmutter, B. (1988) Learning state space trajectories in recurrent neural networks, CMU technical report, CMU-CS-88–191. {arSJH}Google Scholar
Phillips, W. A. (1974) On the distinction between sensory storage and short-term visual memory. Perception and Psychophysics 16:283–90. {WAP}Google Scholar
(1989) Human cognition and neural computation. In: Neural networks: From models to applications, ed. Personnaz, L. & Dreyfus, G.. I.D.S.E.T. Paris, France. {WAP}Google Scholar
Pineda, F. J. (1987) Generalization of back-propagation to recurrent neural networks. Physical Review Letters 19(59): 2229–32. {rSJH}Google Scholar
(1988) Generalization of backpropagation to recurrent and high-order networks. In: Proceedings of the IEEE Conference on Neural Information Processing Systems-Natural and Synthetic, ed. Anderson, D.. American Institute. {aSJH}Google Scholar
Pinker, S. & Prince, A. (1988) On language and connectionism: Analysis of a parallel distributed processing model of language acquisition. MIT Technical Report, Cognition 28:73–193. {aSJH, NC, KH, JKK}Google Scholar
Pollack, J. & Waltz, D. (1982) Natural language processing using spreading activation and lateral inhibition. In: Proceedings of the Fourth Annual Cognitive Science Conference. Ann Arbor, MI. {aSJH}Google Scholar
Pribram, K. H. (1971) Languages of the brain. Prentice-Hall. {BB}Google Scholar
Pylyshyn, Z. (1973) What the mind’s eye tells the mind’s brain: A critique of mental imagery. Psychological Bulletin 80:1–24. {WAP}Google Scholar
(1984) Computation and cognition: Toward a foundation for cognitive science. Bradford Books/MIT Press. {aSJH, NC}Google Scholar
Rail, W. (1989) Cable theory for dendritic neurons. In: Methods in neuronal modeling, ed. Koch, C. & Segev, I.. Bradford Books/MIT Press. {BB}Google Scholar
Ratcliff, R. (in press) Connectionist models of recognition memory: Constraints imposed by learning and forgetting functions. Psychology Review 97. {JKK}Google Scholar
Rescorla, R. A., & Wagner, A. R. (1972) A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In: Classical conditioning II: Current theory and research, ed. Black, A. H. & Prokasy, W. F.. Appleton-Century-Crofts. {WSM}Google Scholar
Rivest, R., Haussler, D. & Warmuth, M. K. (1989) Proceedings of second annual workshop on computational learning theory. Morgan-Kaufman. {aSJH}Google Scholar
Robinson, D. L., Goldberg, M. E. & Stanton, G. B. (1978) Parietal association cortex in the primate: Sensory mechanisms and behavioral modulations. Journal of Neurophysiology 41:910–32. {SB}Google Scholar
Roiblat, H. L. (1982) The meaning of representation in animal memory. Behavioral and Brain Sciences 5:353–72. {WSM}Google Scholar
Rosch, E., Simpson, C, & Miller, R. S. (1976) Structural bases of typicality effects. Journal of Experimental Psychology: Human Perception and Performance 2:491–502. {JKK}Google Scholar
Rosenberg, C. R. (1987) Analysis of NETtalk’s internal structure. Ninth Annual Cognitive Science Conference, August, Seattle, WA. {aSJH, RMG}Google Scholar
Rosenblatt, F. (1962) Principles of neuro-dynamics. Spartan. {aSJH, BB}Google Scholar
Rumelhart, D. E. (1987) Personal communication. {aSJH}Google Scholar
Rumelhart, D. E., Hinton, G. E. & Williams, E. (1986a) Learning internal representations by error propagation. Nature 323:533–36. {aSJH}Google Scholar
(1986b) Learning representations by back-propagating errors. Nature 323:533–36. {JKK}Google Scholar
Rumelhart, D. E., & McClelland, J. L. (1986a) Parallel distributed processing: Explorations in tee microstructure of cognition, vol. 1: Foundations. Bradford Books/MIT Press. {aSJH}Google Scholar
(1986b) On learning the past tenses of English verbs. In: Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1, ed. McClelland, J. L. & Rumelhart, D. E.. MIT Press. {KH}Google Scholar
(1986c) PDF models and general issues in cognitive science. In: Parallel distributed processing, vol. 1, ed. Rumelhart, D. E. & McClelland, J. L.. MIT Press. {JKK, WSM}Google Scholar
Rumelhart, D. E., Smolensky, P., McClelland, J. L. & Hinton, G. E. (1986) Schemata and sequential thought processes in PDP models. In: Parallel distributed processing: Explorations in the microstructures of cognition, vol. 2: Psychological and biological processes, ed. McClelland, J. L. & Rumelhart, D. E.. MIT Press. {NC}Google Scholar
Schank, R. C. (1975) Conceptual information processing. North Holland. {aSJH}Google Scholar
Schlimmer, J. C. (1987) Incremental adjustment of representations for learning. Proceedings of the Fourth International Workshop on Machine Learning, Irvine, CA. Morgan Kaufmann. {PL}Google Scholar
Schneider, W. & Detweiler, M. (1987) A connectionist control architecture for working memory. In: The psychology of learning and motivation, vol. 21, ed. Bower, G. H.. Academic Press. {KH}Google Scholar
Searle, J. (1980) Minds, brains, and programs. Behavioral and Brain Sciences 3:417–57. {WAP}Google Scholar
I., Segev., Fleshman, J. W. & Burke, R. E. (1989) Compartmental models of complex neurons. In: Methods in neuronal modeling, ed. Koch, C. & Segev, I.. Bradford Books/MIT Press. {BB}Google Scholar
Seidenberg, M. S. & McClelland, J. L. (1989) A distributed, developmental model of word recognition and naming. Psychological Review 96:523—68. {KH}.Google Scholar
Sejeowski, T. & Rosenberg, C. (1986) NET talk: A parallel network that learns to read aloud. The Johns Hopkins University EE and CS Technical Report, January. {aSJH}Google Scholar
(1986) NETtalk: A parallel network that learns to read aloud. Technical Report JHU/EECS-86/01, Department of Electrical Engineering and Computer Science, Johns Hopkins University. {KH}Google Scholar
Shastri, L. (1985) Evidential reasoning in semantic networks: A formal theory and its parallel implementation. Technical Report 166, September. Department of Computer Science, University of Rochester. {NC}Google Scholar
Shepard, R. N. (1962) The analysis of proximities: Multidimensional scaling with an unknown distance function II. Psychometrika 27:219–46. {aSJH}Google Scholar
(1987) Toward a universal law of generalization for psychological science. Science 237:1317–23. {aSJH}Google Scholar
(1988) How fully should connectionism be activated? Two sources of excitation and one of inhibition. Behavioral and Brain Sciences 11:52. {KH}Google Scholar
(1989) Internal representation of universal regularities: A challenge for connectionism. In: Neural connections and mental computation, ed. Nadel, L., Cooper, L. A., Culicover, P., & Harnish, R. M.. MIT Press/Bradford Books. {aSJH}Google Scholar
Shepard, R. N., Hovland, C. L. & Jenkins, H. M. (1961) Learning and memorization of classifications. Psychological Monographs 75(13):517. {JKK}Google Scholar
Shepard, R. N., Romney, A. K. & Nerlove, S. B. (1972) Multidimensional scaling. Seminar Press. {aSJH}Google Scholar
Shepherd, G. M., & Brayton, R. K. (1987) Logic operations are properties of computer-simulated interactions between excitable dendritic spines. Neuroscience 21:151–66. {aSJH}Google Scholar
Sherrington, D. (ed.) (1989) Special issue in memory of Elizabeth Gardner. Journal of Physiology A: Mathematics General 22. {GGT}Google Scholar
Shvaytser, H. (1989) On exact learning from a finite number of positive examples. Unpublished manuscript. {rSJH}Google Scholar
Singhal, S. (1988) The completely connected network. Bellcore Technical Report. {aSJH}Google Scholar
Skinner, B. F. (1953) Science and human behavior. The Free Press. {aSJH}Google Scholar
(1957) Verbal behavior. Appleton-Century-Crofts. {aSJH}Google Scholar
Sidman, M. & Tailby, W. (1982) Conditional discrimination vs. matching to sample: An expansion of the testing paradigm. Journal of the Experimental Analysis of Behaviour 37:5–22. {GDAB}Google Scholar
Smolensky, P. (1987) On variable binding and the representation of symbolic structures in connectionist systems. Technical Report CU-CS-355–87, Department of Computer Science and Institute for Cognitive Science, University of Colorado, Boulder. {NC}Google Scholar
(1988) On the proper treatment of connectionism. Behavioral and Brain Sciences 11(1): 1–59. {arSJH, KH}Google Scholar
Specht, D. F. (1967) Generation of polynomial discriminant functions for pattern recognition. IEEE Transactions Electronic Computers, EC-16(3):308–19. {aSJH}Google Scholar
Stork, D. G. (1989) Is back propagation biologically plausible? In: International Joint Conference on Neural Networks IEEE, vol. II. {JKK}Google Scholar
Strong, G. W., & Whitehead, B. A. (1989) A solution to the tag-assignment problem for neural networks. Behavioral and Brain Sciences 12:381–97. {JER}Google Scholar
Sutton, R. S., & Barto, A. G. (1981) Toward a modern theory of adaptive networks: Expectation and prediction. Psychological Review 99:135–70. {WSM}Google Scholar
Tomko, G. J., & Crapper, D. R. (1974) Neural variability: Non-stationary response to identical visual stimuli. Brain Research 79:405–18. {aSJH}Google Scholar
Touretzky, D. S., & Hinton, G. E. (1985) Symbols among the neurons: Details of a connectionist inference architecture. In: Proceedings of the Ninth International Joint Conference on Artificial Intelligence, University of California at Los Angeles. {NC}Google Scholar
Touretzky, D. S., & Wheeler, D. (1989) A computational basis for phonology. IEEE Conference on Neural Information Processing Systems. {JER}Google Scholar
Tukey, J. (1977) Exploratory data analysis. Addison-Wesley. {aSJH}Google Scholar
Tversky, A. (1977) Features of similarity. Psychological Review 84:327—52. {aSJH, JKK}Google Scholar
Utgoff, P. (1986) Machine learning of inductive bias. Kluwer. {PL}Google Scholar
Valiant, L. G. (1984) A theory of the learnable. Communications of Association for Computing Machinery 27(11): 1134–42. {rSJH}Google Scholar
(1985) Learning disjunctions of conjunctions. Proceedings of the Ninth International Joint Conference on Artificial Intelligence 560–566, August 18–23, Los Angeles, CA. {aSJH}Google Scholar
Van Gelder, T. J. (1990) Compositionality: A connectionist variation on a classical theme. Cognitive Science 14(2). {TVG}Google Scholar
(1990) What is the “D” in PDP? A survey of the concept of distribution. In: Philosophy and connectionist theory, ed. Stich, S., Ramsey, W. & Rumelhart, D. E.. Lawrence Erlbaum. {TVG}Google Scholar
Von der Malsburg, C. (1988) Pattern recognition by labeled graph matching. Neural Networks 1:141–48. {WAP}Google Scholar
Werbos, P. J. (1974) Beyond regression: New tools for prediction and analysis in the behavioral sciences. Ph.D. dissertation thesis, Harvard University. {aSJH}Google Scholar
White, H. (1989) Learning in artificial neural networks: A statistical perspective. Discussion paper 89–49. Economics Department, University of California at San Diego. Submitted for publication. {RMG}Google Scholar
Widrow, B. & Hoff, M. E. (1960) Adaptive switching circuits 1960 WESCON Convention Record, Part IV, 96–104. {aSJH}Google Scholar
Wieland, A. & Leighton, R. (1987) Geometric analysis of neural network capabilities. Neural Networks Conference, San Diego, CA {aSJH}Google Scholar
Williams, R. J. (1986) The logic of activation functions. In: Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1: Foundations, ed. Rumelhart, D. E., & McClelland, J. L.. Bradford Books/MIT Press. {aSJH}Google Scholar
Williams, R. J., & Zipser, D. (1988) A learning algorithm for continually running fully recurrent neural networks. Technical Report ICS Report 8805, University of California at San Diego. {aSJH}Google Scholar
(1989) A learning algorithm for continually running fully recurrent neural networks. Neural Computation 1:270–80. {rSJH}Google Scholar
Wilson, M. A., & Bower, J. M. (1989) The simulation of large-scale neural networks. In: Methods in neuronal modeling, ed. Koch, C. & Segev, I.. Bradford Books/MIT Press. {BB}Google Scholar
Winder, R. O. (1962) Threshold logic. Ph.D. dissertation. Princeton University. {aSJH}Google Scholar
Yin, T. C. T., & Mountcastle, V. B. (1977) Visual input to the visuomotor mechanisms of the monkey’s parietal lobe. Science 197:1381–83. {SB}Google Scholar
Zentall, T. P., Jagielo, J. A., Jackson-Smith, O. & Urcuioli, P. (1987) Memory codes in pigeon short-term memory: Effects of varying the number of sample and comparison stimuli. Learning and Motivation 18:21—33. {WSM}Google Scholar