A bottom up approach towards the acquisition and expression of sequential representations applied to a behaving real-world device: Distributed adaptive control III

Neural Networks

Volumn 11, Issue 7-8, 1998, Pages 1531-1549

  Verschure, Paul F M J ^a   Voegtlin, Thomas ^b  

^a Institute of Neuroinformatics Zurich   (Switzerland)

^b UNIVERSITÉ DE LYON   (France)

Author keywords

Brain; Conditioning; Distributed Adaptive Control; Foraging; Goal directed behavior; Learning; Model; Problem solving; Robot; Sequential representations

Indexed keywords

BEHAVIORAL RESEARCH; BRAIN MODELS; ROBOT LEARNING; ROBOTS; SENSORS;

DISTRIBUTED ADAPTIVE CONTROL;

ADAPTIVE CONTROL SYSTEMS;

ARTIFICIAL NEURAL NETWORK; BEHAVIOR; CONDITIONING; CONTROL SYSTEM; FEEDING; LEARNING; MODEL; PRIORITY JOURNAL; PROBLEM SOLVING; REVIEW; ROBOTICS;

EID: 0032191778     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0893-6080(98)00029-X     Document Type: Article

References (56)

1. Almassy, N., & Verschure, P. F. M. J. (1992). Optimizing selforganizing control architectures with genetic algorithms: The interaction between natural selection and ontogenesis. In R. Manner, & B. Manderick (Eds.), Proceedings of the second conference on parallel problem solving from nature (pp. 451-460).
2. Armony J., Seruan-Schreiber J., Cohen J., LeDoux J. An anatomically constrained neural network model of fear conditioning. Behavioral Neuroscience. 109:1995;246-257.
3. Baloch A., Waxman A. Visual learning, adaptive expectations, and behavioral conditioning of the mobile robot mavin. Neural Networks. 4:1991;271-302.
4. Bienenstock E., Cooper L., Munro P. Theory for the development of neuron selectivity: Orientation specificity and binocular interaction in visual cortex. Journal of Neuroscience. 2:1982;32-48.
5. Dehaene S., Changeux J.-P. A hierarchical neuronal network for planning behavior. Proceedings of the National Academy of Sciences of the USA. 94:1997;13293-13298.
6. Denham, M., & McCabe, S. (1995). Robot control using temporal sequence learning. In WCNN '95. World Congress on neural networks (Vol. 2, pp. 393-396). Mahwah, NJ: Erlbaum.
7. Dickinson, A. (1994). Instrumental conditioning. In N. Mackintosh (Ed.), Animal learning and cognition (pp. 45-79). San Diego, CA: Academic Press.
8. Dominey P., Arbib M., Joseph J. A model of corticostriatal plasticity for learning oculomotor associations and sequences. Journal of Cognitive Neuroscience. 7(3):1995;311-336.
9. Douglas R., Mahowald M., Mead C. Neuromorphic analogue VLSI. Annual Review of Neuroscience. 18:1995;255-281.
10. Fodor, J. (1983). The modularity of mind. Cambridge, MA: MIT Press.
11. Galuske, R., Singer, W., & Munk, M. (1997). Reticular activation facilitates use-dependent plasticity of orientation preference maps in the cat visual cortex. In Society for Neuroscience meeting (Abstracts, p. 2059). New Orleans, LA.
12. Goldstein, L., & Smith, K. (1991). Bugworld a distributed environment for the study of multi-agent learning algorithms. Technical report, Department of Computer Science, UCSC.
13. Grossberg S. Processing of expected and unexpected events during conditioning and attention: A psychophysical theory. Psychological Review. 89:1982;529-572.
14. Grossberg S., Levine D. Neural dynamics of attentionally modulated Pavlovian conditioning: Blocking, inter-stimulus interval, and secondary reinforcement. Applied Optics. 27:1987;5015-5030.
15. Grossberg S., Schmajuk N. Neural dynamics of attentionally modulated Pavlovian conditioning: Conditioned reinforcement, inhibition, and opponent processing. Psychobiology. 15:1987;195-240.
16. Hall, G. (1994). Pavlovian conditioning: Laws of association. In N. Mackintosh (Ed.), Animal learning and cognition (pp. 15-43). San Diego, CA: Academic Press.
17. Hopfield J. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the USA. 79:1982;2554-2558.
18. Hull, C. (1943). Principles of behavior. New York: Appleton-Century-Crofts.
19. Indeveri, G., & Verschure, P. F. M. J. (1997). Autonomous vehicle guidance using analog VLSI neuromorphic sensors. In W. Gerstner, A. Germond, & M. H. Nicoud (Eds.), Proceedings artificial neural networks-ICANN97, Lausanne, Switzerland (pp. 811-816). Lecture Notes in Computer Science. Berlin: Springer.
20. Kaelbling L., Littman M., Moore A. Reinforcement learning: A survey. Journal of Artificial Intelligence Research. 4:1996;237-285.
21. Klopf, A. (1982). The hedonistic neuron: A theory of memory, learning and intelligence. Washington, DC: Hemisphere.
22. König, P., & Verschure, P. (1995). Subcortical control of the synchronization of cortical activity: A model. In Society for Neuroscience meeting (Abstracts, p. 1651). San Diego, CA.
23. Konorski, I. (1967). Integrative activity of the brain. Chicago, IL: University of Chicago Press.
24. Kröse, B., & Van Dam, J. (1997). Neural vehicles. In O. Omidvar, & P. Van der Smagt (Eds.), Neural systems for robotics (pp. 271-296). New York: Academic Press.
25. Lavond D.G., Kim J.J., Thompson R.F. Mammalian brain substrates of aversive classical conditioning. Annual Review of Psychology. 44:1993;317-342.
26. Mackintosh, N. (1972). The psychology of animal learning. New York: Academic Press.
27. Miller S., Konorski J. Sur une forme particulière des reflexes conditionnels. Comptes Rendus des Seances de la Societé Polonaise de Biologie. 49:1928;1155-1157.
28. Mondada, F., & Verschure, P. F. M. J. (1993). Modeling system-environment interaction: The complementary roles of simulations and real world artifacts. In Proceedings of the second European conference on artificial life (pp. 808-817). Cambridge, MA: MIT Press .
29. Mondada, F., Franzi, E., & Ienne, P. (1993). Mobile robot miniaturisation: a tool for investigation in control algorithms. In Experimental robotics III. Proceedings of the third international symposium on experimental robotics, Kyoto, Japan (pp. 501-513). Berlin: Springer.
30. Moore, M. E. (1956). Gedanken-experiments on sequential machines. In C. E. Shannon, & J. McCarthy (Eds.), Automata studies (pp. 129-153). Princeton, NJ: Princeton University Press.
31. Morita M. Computational study on the neural mechanism of sequential pattern memory. Cognitive Brain Research. 5:1996;137-146.
32. Newell A. Physical symbol systems. Cognitive Science. 4:1980;135-183.
33. Newell, A. (1990). Unified theories of cognition. Cambridge, MA: Harvard University Press.
34. Oja E. A simplified neuron model as a principal component analyzer. Journal of Mathematical Biology. 15:1982;267-273.
35. Pavlov, I. P. (1927). Conditioned reflexes. Oxford: Oxford University Press.
36. Rao R., Ballard D. Dynamic model of visual recognition predicts neural response properties in the visual cortex. Neural Computation. 9:1997;712-763.
37. Roberts, W. (1992). Foraging by rats on a radial maze: Learning, memory, and decision rules. In I. Gormezano, & E. Wasserman (Eds.), Learning and memory: The behavioral and biological substrates (pp. 7-24). Hillsdale, NJ: Erlbaum.
38. Schultz W., Dayan P., Montague P. A neural substrate of prediction and reward. Science. 275:1997;1593-1599.
39. Sutton R. S., Barto A. G. Toward a modern theory of adaptive networks: Expectations and prediction. Psychological Review. 88:1981;135-170.
40. Thompson R. F., Berger T. W., Madden I. V. Cellular processes of learning and memory in the mammalian CNS. Annual Review of Neuroscience. 6:1983;447-491.
41. Thorndike, E. (1911). Animal intelligence. New York: Macmillan.
42. Touretzky, D., & Saksida, L. (1996). Skinnerbots. In P. Maes, M. Mataric, J.-A. Meyer, J. Pollock, & S. Wilson (Eds.), From animals to animats 4: Proceedings of the fourth international conference on simulation of adaptive behavior (pp. 285-294). Cambridge, MA: MIT Press.
43. Verschure P.F.M.J. Smolensky's theory of mind. Behavioral and Brain Sciences. 13:1990;407.
44. Verschure, P. F. M. J. (1992). Taking connectionism seriously: The vague promise of subsymbolism and an alternative. In Proceedings of the 14th annual conference of the Cognitive Science Society, Bloomington, IN (pp. 653-658). Hillsdale, NJ: Erlbaum.
45. Verschure, P. F. M. J. (1993a). The cognitive development of an autonomous behaving artifact: The self-organization of categorization, sequencing, and chunking. In H. Cruze, H. Ritter, & J. Dean (Eds.), Proceedings of prerational intelligence (pp. 95-117). Bielefeld: ZiF.
46. Verschure P.F.M.J. Formal minds and biological brains. IEEE Expert. 8(5):1993;66-75.
47. Verschure, P. F. M. J. (1996). Minds, brains, and robots: Explorations in distributed adaptive control. In A. Soares (Ed.), Proceedings of the second Brazilian international conference on cognitive science.
48. Verschure, P. F. M. J. (1997a). Connectionist explanation: Taking positions in the mind-brain dilemma. In G. Dorffner (Ed.), Neural networks and a new artificial intelligence (pp. 133-188). London: Thompson.
49. Verschure, P. F. M. J. (1997b). Xmorph: A software tool for the synthesis and analysis of neural systems. Technical report, Institute of Neuroinformatics, ETH-UZ.
50. Verschure, P. F. M. J. (1998). Synthetic epistemology: The acquisition, retention, and expression of knowledge in natural and synthetic systems. In Proceedings World conference on computational intelligence 1998, Anchorage, AL.
51. Verschure P.F.M.J., Coolen A.C.C. Adaptive fields: distributed representations of classically conditioned associations. Network. 2:1991;189-206.
52. Verschure, P. F. M. J., & König, P. (1997). Modulation of temporal interactions in cortical circuits. In H.-M. Gross (Ed.), Proceedings of SOAVE97 (pp. 77-88). Dusseldorf: DVI.
53. Verschure, P. F. M. J., & Pfeifer, R. (1992). Categorization, representations, and the dynamics of system-environment interaction: A case study in autonomous systems. In J. A. Meyer, H. Roitblat, & S. Wilson (Eds.), From animals to animats: Proceedings of the second international conference on simulation of adaptive behavior, Honolulu, HI (pp. 210-217). Cambridge, MA: MIT Press.
54. Verschure P.F.M.J., Kröse B., Pfeifer R. Distributed adaptive control: The self-organization of structured behavior. Robotics and Autonomous Systems. 9:1992;181-196.
55. Verschure P.F.M.J., Wray J., Sporns O., Tononi G., Edelman G. Multilevel analysis of classical conditioning in a behaving real world artifact. Robotics and Autonomous Systems. 16:1995;247-265.
56. Weinberger N.M., Javid R., Lepan B. Long term retention of learning-induced receptive field plasticity in the auditory cortex. Proceedings of the National Academy of Sciences of the USA. 90:1993;2394-2398.