Modelling memory functions with recurrent neural networks consisting of input compensation units: I. Static situations

Biological Cybernetics

Volumn 96, Issue 5, 2007, Pages 455-470

  Kühn, Simone ^a   Beyn, Wolf Jürgen ^a   Cruse, Holk ^a  

^a BIELEFELD UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGEBRA; COMPUTATIONAL METHODS; CYBERNETICS; LEARNING SYSTEMS; NEURONS; ONLINE SYSTEMS;

MEMORY FUNCTIONS; ONLINE LEARNING; SYNAPTIC CHANGES;

RECURRENT NEURAL NETWORKS;

ARTICLE; HUMAN; LEARNING; MATHEMATICS; MEMORY; NERVE CELL; NERVE CELL NETWORK; PHYSIOLOGY; PSYCHOLOGICAL MODEL;

HUMANS; LEARNING; MATHEMATICS; MEMORY; MODELS, PSYCHOLOGICAL; NERVE NET; NEURONS;

EID: 34247557168     PISSN: 03401200     EISSN: 14320770     Source Type: Journal    
DOI: 10.1007/s00422-006-0137-x     Document Type: Article

References (65)

1. Amari S (1977) Dynamics of pattern formation in lateral-inhibition type neural fields. Biol Cybern 27:77-87
2. Amit DJ (1995) The hebbian paradigm reintegrated: local reverberations as internal representations. Behav Brain Sci 18:617-626
3. Baddeley A (1986) Working Memory. Clarendon, Oxford
4. Baddeley A (1992) Working memory. Science 255:556-559
5. Baldi P, Pineda F (1991) Contrastive learning and neural oscillator. Neural Comput 3:526-545
6. Beer R (2003) The dynamics of active categorical perception in an evolved model agent. Adapt Behav 11:209-243
7. Berman A, Plemmons RJ (1994) Nonnegative matrices in the mathematical sciences. Classics in Applied Mathematics, vol 9. SIAM, Philadelphia
8. Chomsky NA (1980) Rules and Representation. Columbia University Press, New York
9. Cormier RJ, Greenwood AC, Connor JA (2001) Bidirectional synaptic plasticity correlated with the magnitude of dendritic calcium transients above a threshold. J Neurophysiol 85:399-406
10. Cruse H (2003) The evolution of cognition-a hypothesis. Cogn Sci 27:135-155
11. Cruse H (2005) Neural networks as cybernetic systems. http://193.30.112.98/brain/archive/cruse [2]
12. 2+ signaling requirements for long-term depression in the hippocampus. Neuron 16:825-833
13. Del Giudice P, Fusi S, Mattia M (2003) Modelling the formation of working memory with networks of integrate-and-fire neurons connected by plastic synapses. J Physiol 97:659-681
14. Doya K (1995) Recurrent networks: supervised learning. In: Arbib M (ed) The Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge, MA, pp 796-800
15. Durstewitz D, Seamans JK, Sejnowski TJ (2000) Neurocomputational models of working memory. Nat Neurosci Suppl 3:1184-1191
16. Feldman J, Narayanan S (2004) Embodied meaning in a neural theory of language. Brain Lang 89:385-392
17. Fogassi L, Ferrari PF, Gesierich B, Rozzi S, Chersi F, Rizzolatti G (2005) Parietal lobe: from action organization to intention understanding. Science 308:662-666
18. Freyd JJ, Finke RA (1984) Representational momentum. J Exp Psychol Learn Memory Cogn 10:126-132
19. Fuster JM (1973) Unit activity in the prefrontal cortex during delayed response performance: neuronal correlates of transient memory. J Neurophysiol 36:61-78
20. Fuster JM (1995) Memory in the Cerebral Cortex: an Empirical Approach to Neural Networks in the Human and Nonhuman Primate. MIT Press, Cambridge, MA
21. Fuster JM, Alexander GE (1971) Neuron activity related to short-term memory. Science 173:652-654
22. Gallese V, Lakoff G (2005) The brain's concepts: the role of the sensory-motor system in conceptual knowledge. Cogn Neuropsychol 22:455-479
23. Giurfa M, Zhang S, Jenett A, Menzel R, Srinivasan M (2001) The concepts of 'sameness' and 'difference' in an insect. Nature 410:930-933
24. Helmchen F (1999) Dendrites as biochemical compartments. In: Stuart G, Spruston N, Häusser M (eds) Dendrites. Oxford University Press, Oxford, pp 161-192
25. Hertz J, Krogh A, Palmer RG (1991) Introduction to the Theory of Neural Computation. Addison-Wesley, Redwood City, CA
26. Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 79:2554-2558
27. Hopfield JJ (1984) Neurons with graded response have collective computational properties like those of two-state neurons. Proc Natl Acad Sci USA 81:3088-3092
28. Jaeger H, Haas H (2004) Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 2:78-80
29. Johnson-Laird PN (1983) Mental models: towards a cognitive science of language, inference, and consciousness. Cambridge University Press, Cambridge, UK
30. Kalveram KT (2000) Sensorimotor sequential learning by a neural network based on redifined Hebbian learning. In: Malgrem H, Borga M, Niklasson L (eds) Perspectives in Neural Computing-Proceedings of the ANNIMAB-1 conference. Springer, London, pp 271-276
31. Kandel ER, Schwartz JH, Jessell TM (2000) Principles of Neural Science. McGraw-Hill, New York
32. Kirkwood A, Dudek SM, Gold JT, Aizenman CD, Bear MF (1993) Common forms of synaptic plasticity in the hippocampus and neocortex in vitro. Science 260:1518-1521
33. Kühn S, Cruse H (2005) Static mental representations in recurrent neural networks for the control of dynamic behavioural sequences. Connect Sci 17:343-360
34. Kühn S, Cruse H (2007) Modelling memory function with recurrent neural networks consisting of input compensation units II - dynamic situations. Biol Cybern (in press)
35. Lisman JE (1989) A mechanism for the Hebb and the anti-Hebb processes underlying learning and memory. Proc Natl Acad Sci USA 86:9574-9578
36. Maass W, Natschläger T, Markram H (2002) Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput 14:2531-2560
37. Marcus G, Vijayan S, Bandi Rao S, Vishton PM (1999) Rule learning by seven-month-old infants. Science 283:77-80
38. Marcus GF, Brinkman U, Clahsen H, Wiese R, Pinker S (1995) German inflection: the exception that proves the rule. Cogn Psychol 29:189-256
39. Marcus GF (2001) The Algebraic Mind: Integrating Connectionism and Cognitive Science. MIT Press, Cambridge, MA
40. Mel BW (1999) Why have dendrites? A computational perspective. In: Stuart G, Spruston N, Häusser M (eds) Dendrites. Oxford University Press, Oxford, pp 271-289
41. Miyashita Y, Hayashi T (2000) Neural representation of visual objects: encoding and top-down activation. Curr Opin Neurobiol 10:187-194
42. Montgomery JM, Madison DV (2004) Discrete synaptic states define a major mechanism of synaptic plasticity. Trends Neurosci 27:744-750
43. Movellan J (1990) Contrastive Hebbian learning in the continuous Hopfield model. In: Touretzky D, Elman J, Sejnowski T, Hinton G (eds) Proceedings of the 1990 Connectionist Models Summer School. Morgan Kaufmann, San Mateo, CA, pp 10-17
44. Narayanan S (1999) Moving right along: a computational model of metaphoric reasoning about events. In: Proceedings of the national conference on artificial intelligence AAAI-99, Orlando, FL. http://www.icsi.berkeley.edu/ ~snarayan/met.ps
45. Niki H (1974a) Prefrontal unit activity during delayed alternation in the monkey: I. Relation to direction of response. Brain Res 68:185-196
46. Niki H (1974b) Prefrontal unit activity during delayed alternation in the monkey: II. Relation to absolute versus relative direction of response. Brain Res 68:197-204
47. Pinker S (1991) Rules of language. Science 253:530-535
48. Pinker S, Prince A (1988) On language and connectionism-analysis of a parallel distributed-processing model of language-acquisition. Cognition 28:73-193
49. Rumelhart DE, Hinton GE, Williams R.T (1986) Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL (eds) Parallel distributed processing, vol 1: Foundations. MIT Press, Cambridge, MA, pp 318-362
50. 3) time complexity learning algorithm for fully recurrent continually running networks. Neural Comput 4:243-248
51. Seidenberg MS, Elman JL (1999) Networks are not 'hidden rules'. Trends Cogn Sci 3:288-289
52. Sejnowski TJ, Koch C, Churchland PS (1988) Computational neuroscience. Science 241:1299-1306
53. Seung HS, Lee DD, Reis BY, Tank DW (2000) Stability of the memory of eye position in a recurrent network of conductance-based model neurons. Neuron 26:259-271
54. Steil JJ (1999) Input-output stability of recurrent neural networks. Cuvillier, Göttingen
55. Steinkühler U, Cruse H (1998) A holistic model for an internal representation to control the movement of a manipulator with redundant degrees of freedom. Biol Cybern 79:457-466
56. Strauss R, Pichler J (1988) Persistence of orientation towards a temapraily invisible landmark in Drosophila melanogaster. J Comp Physiol A 182:411-423
57. Umiltà MA, Kohler E, Gallese V, Fogassi L, Fadiga L, Keysers C, Rizzolatti G (2001) "T know what you are doing": a neurophysiological study. Neuron 32:91-101
58. Wang X-J (2001) Synaptic reverberation underlying mnemonic persistent activity. Trends Neurosci 24:455-463
59. Widrow B, Hoff ME (1960) Adaptive switching circuits. In: 1960 WESCON Convention Record Part IV. Institute of Radio Engineers, New York, pp 96-104
60. Williams RJ, Zipser D (1989a) A learning algorithm for continually running fully recurrent neural networks. Neural Comput 1:270-280
61. Williams RJ, Zipser D (1989b) Experimental analysis of the real-time recurrent learning algorithm. Connect Sci 1:87-111
62. Wilson H, Cowan J (1973) A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue. Kybernetik 13:55-80
63. Wolpert DM, Kawato M (1998) Multiple paired forward and inverse models for motor control. Neural Netw 11:1317-1329
64. Xie X, Seung HS (2003) Equivalence of backpropagation and contrastive Hebbian learning in layered networks. Neural Comput 15:441-454
65. Zipser D, Kehoe B, Littlewort G, Fuster J (1993) A spiking network model of short-term active memory. J Neurosci 13:3406-3420