Building a world model with structure-sensitive sparse binary distributed representations

Biologically Inspired Cognitive Architectures

Volumn 3, Issue , 2013, Pages 64-86

  Rachkovskij, Dmitri A ^a   Kussul, Ernst M ^b   Baidyk, Tatiana N ^b  

^a International Research and Training Center for Information Technologies and Systems of the National Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine   (Ukraine)

^b UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO   (Mexico)

Author keywords

Associative memory; Associative Projective Neural Networks; Binding; Distributed representations; Hierarchical relational structures; Vector symbolic architectures

Indexed keywords

ASSOCIATIVE MEMORY; AUTO-ASSOCIATIVE MEMORY; BINDING; COGNITIVE ARCHITECTURES; DISTRIBUTED REPRESENTATION; INTERNAL REPRESENTATION; RELATIONAL STRUCTURES; STRUCTURE-SENSITIVE;

ASSOCIATIVE PROCESSING; ASSOCIATIVE STORAGE; COGNITIVE SYSTEMS; DECODING; NEURAL NETWORKS; VECTORS;

STRUCTURAL DESIGN;

EID: 84877991281     PISSN: 2212683X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.bica.2012.09.004     Document Type: Article

References (163)

1. J.S. Albus A new approach to manipulator control: The cerebellar model articulation controller (CMAC) Transaction of the ASME, The Journal of Dynamic Systems, Measurement, and Control 97 1975 220 227
2. J.S. Albus A model of computation and representation in the brain Information Sciences 180 2010 1519 1554
3. D.J. Amit Modeling brain function: The world of attractor neural networks 1989 Cambridge University Press
4. D.J. Amit The hebbian paradigm reintegrated: Local reverberations as internal representations Behavioral and Brain Science 18 1995 617 657
5. N.M. Amosov Modelling of thinking and the mind 1967 Spartan Books New York
6. N.M. Amosov Algorithms of the mind 1979 Naukova Dumka Kiev (in Russian)
7. Amosov, N. M.; Kussul, E. M. (1969). Possible structure of system for reinforcement and inhibition. In Problems of heuristic modelling. Inst. of Cybernetics, Ukrainian Acad. Sci. (Vol. 1, pp. 3-11) (in Russian).
8. N.M. Amosov, T.N. Baidyk, A.D. Goltsev, A.M. Kasatkin, L.M. Kasatkina, E.M. Kussul, and D.A. Rachkovskij Neurocomputers and intelligent robots 1991 Naukova Dumka Kiev (in Russian)
9. Amosov, N. M.; Kussul, E. M.; Fomenko, V. D. (1975). Transport robot with a neural network control system. Advance papers of the Fourth Intern. Joint Conference on, Artificial intelligence (Vol. 9, pp. 1-10).
10. N.M. Amosov, A.M. Kasatkin, L.M. Kasatkina, E.M. Kussul, S.A. Talayev, and V.D. Fomenko Intelligent behaviour systems based on semantic networks Kybernetes 2 4 1973 211 216
11. N.M. Amosov, A.M. Kasatkin, L.M. Kasatkina, and S.A. Talayev Automata and the mindful behaviour 1973 Naukova Dumka Kiev pp. 374 (in Russian)
12. J.A. Anderson Cognitive and psychological computation with ceural models IEEE transactions on Systems, Man, and Cybernetics, SMC 13 5 1983 799 814
13. J.R. Anderson, and G.H. Bower Human associative memory 1973 V.H. Winston Washington, DC
14. J.R. Anderson, and C. Lebiere The Newell test for a theory of cognition Behavioral and Brain Sciences 26 2003 587 640
15. J.A. Anderson, and G.L. Murphy Psychological concepts in a parallel system Physica 22D 1986 318 336
16. Yu.G. Antomonov Systems, complexity, dynamics 1969 Naukova Dumka Kiev (in Russian)
17. Yu.G. Antomonov Principles of neurodynamics 1974 Naukova Dumka Kiev (in Russian)
18. T.N. Baidyk Neural networks and problems of Artificial Intelligence 2002 Naukova Dumka Kiev (in Russian)
19. Baidyk, T.N.; Kussul, E.M. (1992). Structure of neural assembly. In Proceedings of the RNNS/IEEE symposium on neuroinformatics and neurocomputers. Rostov-on-Don, Russia (pp. 423-434).
20. T. Baidyk, E. Kussul, O. Makeyev, A. Caballero, L. Ruiz, G. Carrera, and G. Velasco Flat image recognition in the process of microdevice assembly Pattern Recognition Letters 25 1 2004 107 118
21. H.B. Barlow Cerebral cortex as model builder D. Rose, V.G. Dodson, Models of the visual cortex 1985 Wiley 37 46
22. L.W. Barsalou Perceptual symbol systems Behavioral and Brain Sciences 22 1999 577 609
23. L.W. Barsalou Simulation, situated conceptualization, and prediction Philosophical Transactions of the Royal Society B 364 1521 2009 1281 1289
24. G. Booch Object-oriented analysis and design 1994 Benjamin/Cummings Menlo Park, California
25. M.M. Botvinick Hierarchical models of behavior and prefrontal function Trends in Cognitive Sciences 12 5 2008 201 208
26. M.M. Botvinick, Y. Niv, and A.C. Barto Hierarchically organized behavior and its neural foundations: a reinforcement learning perspective Cognition 113 3 2009 262 280
27. V. Braitenberg Cell assemblies in the cerebral cortex R. Heim, G. Palm, Theoretical approaches to complex systems 1978 Springer Verlag New York 171 188
28. A. Browne, and R. Sun Connectionist inference models Neural Networks 14 10 2001 1331 1355
29. A. Bubic, D.Y. von Cramon, and R.I. Schubotz Prediction, cognition and the brain Frontiers in Human Neuroscience 4 25 2010
30. M.V. Butz, E. Shirinov, and K. Reif Self-organizing sensorimotor maps plus internal motivations yield animal-like behavior Adaptive Behavior 18 3-4 2010 315 337
31. G. Buzsáki Neural syntax: cell assemblies, synapsembles and readers Neuron 68 3 2010 362 385
32. S. Clark, and J.R. Curran Wide-coverage efficient statistical parsing with CCG and log-linear models Computational Linguistics 33 4 2007 493 552
33. Dayan, P. (1999). Unsupervised learning. In R. A. Wilson & F. Keil (Eds). The MIT encyclopedia of the cognitive sciences.
34. E. Dietrich, and A.B. Markman Discrete thoughts: Why cognition must use discrete representations Mind and Language 18 1 2003 95 119
35. L.A. Doumas, J.E. Hummel, and C.M. Sandhofer A theory of the discovery and predication of relational concepts Psychological Review 115 1 2008 1 43
36. C. Eliasmith, and C.H. Anderson Neural engineering: Computation, representation and dynamics in neurobiological systems 2003 MIT Press Cambridge, MA
37. C. Eliasmith, and P. Thagard Integrating structure and meaning: A distributed model of analogical mapping Cognitive Science 25 2 2001 245 286
38. B. Falkenhainer, K.D. Forbus, and D. Gentner The structure-mapping engine: algorithm and examples Artificial Intelligence 41 1989 1 63
39. S. Fidler, M. Boben, and A. Leonardis Learning hierarchical compositional representations of object structure S. Dickinson, A. Leonardis, B. Schiele, M.J. Tarr, Object categorization: Computer and human vision perspectives 2009 Cambridge University Press 196 215
40. P. Foldiak Forming sparse representations by local anti-Hebbian learning Biological Cybernetics 64 1990 165 170
41. P. Foldiak, and M.P. Young Sparse coding in the primate cortex M.A. Arbib, Handbook of brain theory and neural networks 1995 MIT Press Cambridge, MA 895 898
42. K.D. Forbus, D. Gentner, and K. Law MAC/FAC: A model of similarity-based retrieval Cognitive Science 19 2 1995 141 205
43. A.A. Frolov, D. Husek, and I.P. Muraviev Informational capacity and recall quality in sparsely encoded Hopfield-like neural network: Analytical approaches and computer simulation Neural Networks 10 5 1997 845 855
44. A.A. Frolov, D. Husek, I.P. Muraviev, and P.Y. Polyakov Boolean factor analysis by attractor neural network IEEE Transactions on Neural Networks 18 3 2007 698 707
45. A.A. Frolov, D. Husek, and P.Y. Polyakov Recurrent-Neural-Network-Based boolean factor analysis and its application to word clustering IEEE Transactions on Neural Networks 20 7 2009 1073 1086
46. A.A. Frolov, and I.P. Murav'ev Informational characteristics of neural networks capable of associative learning based on Hebbian plasticity Network 4 1993 495 536
47. V. Gallese, and G. Lakoff The Brain's concepts: The role of the Sensory-motor system in conceptual knowledge Cognitive Neuropsychology 22 3 2005 455 479
48. Gasser, M.; Colunga, E. (2001). Learning relational correlations. International Conference on Cognitive Modeling. (Vol. 4, pp. 91-96).
49. R.W. Gayler Vector symbolic architectures are a viable alternative for Jackendoff's challenges Behavioral and Brain Sciences 29 1 2006 78 79
50. R. Gayler Multiplicative binding, representation operators, and analogy K. Holyoak, D. Gentner, B. Kokinov, Advances in analogy research: Integration of theory and data from the cognitive, computational, and neural sciences 1998 New Bulgarian University Sofia, Bulgaria 405
51. R.W. Gayler Vector symbolic architectures answer Jackendoff's challenges for cognitive neuroscience Peter Slezak, ICCS/ascs international conference on cognitive science 2003 University of New South Wales Sydney, Australia 133 138
52. S. Gerth, and P. beim Graben Unifying syntactic theory and sentence processing difficulty through a connectionist minimalist parser Cognitive Neurodynamics 3 4 2009 297 316
53. A. Goltsev An assembly neural network for texture segmentation Neural Networks 9 4 1996 643 653
54. A. Goltsev Neural networks with assembly organization 2005 Naukova Dumka Kiev 200 p
55. S. Harnad The symbol grounding problem Physica D 42 1990 335 346
56. D.O. Hebb The organization of behavior 1949 Wiley New York
57. G.E. Hinton Mapping part-whole hierarchies into connectionist networks Artificial Intelligence 46 1990 47 76
58. Hinton, G. E.; McClelland, J. L.; Rumelhart, D. E. (1986). Distributed representations. In D. E. Rumelhart, J. L. McClelland, & the PDP research group (Eds.). Parallel distributed processing: Exploration in the microstructure of cognition (Vol: Foundations, pp. 77-109). Cambridge, MA: MIT Press.
59. K.J. Holyoak, and P. Thagard Analogical mapping by constraint satisfaction Cognitive Science 13 1989 295 355
60. B. Hommel, J. Müsseler, G. Aschersleben, and W. Prinz The theory of event coding (TEC): A framework for perception and action planning Behavioral and Brain Science 24 4 2002 849 937
61. J.J. Hopfield Neural networks and physical systems with emergent collective computational abilities Proceedings of the National Academy of Sciences, USA 79 1982 2554 2558
62. J.E. Hummel Getting symbols out of a neural architecture Connection Science 23 2 2011 109 118
63. J.E. Hummel, and K.J. Holyoak Distributed representations of structure: A theory of analogical access and mapping Psychological Review 104 1997 427 466
64. R. Jackendoff Foundations of language: Brain, meaning, grammar, evolution 2002 Oxford University Press Oxford, UK
65. B.T. Johns, and M.N. Jones Perceptual inference through global lexical similarity Topics in Cognitive Science 4 1 2012 103 120
66. W.B. Johnson, and J. Lindenstrauss Extensions of Lipshitz mapping into Hilbert space Contemporary Mathematics 26 1984 189 206
67. Kadone, H.; Nakamura, Y. (2006). Segmentation, memorization, recognition and abstraction of humanoid motions based on correlations and associative memory. In 6th IEEE-RAS International Conference on Humanoid Robots, 4-6 December, 2006 (pp. 1-6).
68. M.J. Kahana Associative symmetry and memory theory Memory and Cognition 30 6 2002 823 840
69. P. Kanerva Sparse distributed memory 1988 MIT Press Cambridge, MA
70. Kanerva, P. (1996). Binary spatter-coding of ordered k-tuples. In C. von der Malsburg, W. von Seelen, J. Vorbruggen, & B. Sendhoff (Eds.). Artificial neural networks, Proceedings of ICANN 96 (pp. 869-873). Springer-Verlag, Berlin.
71. P. Kanerva Hyperdimensional computing: An introduction to computing in distributed representation with highdimensional random vectors Cognitive Computation 1 2009 139 159
72. Kanerva, P.; Kristoferson, J.; Holst, A. (2000). Random indexing of text samples for Latent Semantic Analysis. In L. R. Gleitman & A. K. Josh (Eds.). Proc. 22nd annual conference of the cognitive science society (p. 1036). Mahwah, New Jersey: Erlbaum.
73. Kaplan, R. M.; Riezler, S.; King, T. H.; Maxwell, J. T.; Vasserman, A. (2004). Speed and accuracy in shallow and deep stochastic parsing. In Proceedings of HLT-NAACL'04 (pp. 97-104). Boston, MA.
74. A. Knoblauch, G. Palm, and F.T. Sommer Memory capacities for synaptic and structural plasticity Neural Computation 22 2 2010 289 341
75. E.M. Kussul Associative neuron-like structures 1992 Naukova Dumka Kiev (in Russian)
76. Kussul, E. M.; Baidyk, T. N. (1993). On information encoding in associative-projective neural networks. (Preprint 93-3). Kiev, Ukraine: V. M. Glushkov Institute of Cybernetics (in Russian).
77. E.M. Kussul, T.N. Baidyk, and D.C. Wunsch Neural networks and micro mechanics 2010 Springer New York 210 p
78. E.M. Kussul, T.N. Baidyk, D.C. Wunsch, O. Makeyev, and A. Martin Permutation coding technique for image recognition systems IEEE Transactions on Neural Networks 17 6 2006 1566 1579
79. Kussul, E. M.; Rachkovskij, D. A.; Baidyk, T. N. (1991). Associative-Projective Neural Networks: architecture, implementation, applications. In Proceedings of the fourth international conference neural networks & their applications (pp. 463-476).
80. Kussul, E. M.; Baidyk, T. N.; Lukovich, V. V.; Rachkovskij, D. A. (1994). Adaptive high performance classifier based on random threshold neurons. In Proc. of EMCSR-94 (pp. 1687-1695).
81. Kussul, E.; Baidyk, T.; Wunsch, D.; Makeyev, O.; Martin, A. (2006b). Image recognition systems based on random local descriptors, IJCNN 2006, Vancouver, Canada, July 16-21, 2006 (pp. 4722-4727).
82. Kussul, E.; Baidyk, T.; Makeyev, O. (2007). Pairwise permutation coding neural Classifier. IEEE international joint conference on neural networks, IJCNN 2007, Orlando, Florida, USA, August 12-17 2007 (pp. 1471.1-1471.6).
83. Kussul, E.; Makeyev, O.; Baidyk, T.; Calderon Reyes, D. (2010b). Neural network with ensembles. In WCCI 2010, IEEE World Congress on Computational Inteligence (IJCNN 2010), July 18-23, 2010, Barcelona, Spain (pp. 2955-2961).
84. E.M. Kussul, and D.A. Rachkovskij Multilevel assembly neural architecture and processing of sequences A.V. Holden, V.I. Kryukov, Neurocomputers and Attention. Connectionism and neurocomputers Vol. II 1991 Manchester University Press Manchester and New York 577 590
85. J.E. Laird The Soar cognitive architecture AISB Quarterly 134 June 2012 1 4
86. G. Lakoff Women, fire, and dangerous things: What categories reveal about the mind 1987 University of Chicago Press
87. T.K. Landauer, and S.T. Dumais Solution to Plato's problem: The latent semantic analysis theory of acquisition, induction and representation of knowledge Psychological Review 104 2 1997 211 240
88. P. Langley, J.E. Laird, and S. Rogers Cognitive architectures: Research issues and challenges Cognitive Systems Research 10 2 2009 141 160
89. A. Lansner Associative memory models: From the cell-assembly theory to biophysically detailed cortex simulations Trends in Neurosciences 32 3 2009 178 186
90. A. Lansner, and O. Ekeberg Reliability and speed of recall in an associative network IEEE Transactions on Pattern Analysis and Machine Intelligence 7 1985 490 498
91. C. Latimer, and C. Stevens Some remarks on wholes, parts and their perception Psycoloquy 8 13 1997
92. Levy, S. D. (2007). Changing semantic role representations with holographic memory. In Computational approaches to representation change during learning and development: Papers from the 2007 AAAI Symposium. Technical Report FS-07-04, AAAI Press.
93. Levy, S. D. & Gayler, R. (2008). Vector Symbolic Architectures: A new building material for artificial general intelligence. In Proceedings of the first conference on artificial general intelligence (AGI-08). IOS Press.
94. Li, P.; Hastie, T. J.; Church, K. W. (2006). Very sparse random projections. In 12th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 287-296). Philadelphia, PA, USA: ACM Press.
95. R. Linsker Self-organization in a perceptual network Computer 21 1988 105 117
96. O. Makeyev, E. Sazonov, T. Baidyk, and A. Martin Limited receptive area neural classifier for texture recognition of mechanically treated metal surfaces Neurocomputing 71 7-9 2008 1413 1420
97. R.P. Mariner, J.E. Laird, and R.L. Lewis A computational unification of cognitive behavior and emotion Cognitive Systems Research 10 2009 48 69
98. A.B. Markman, and E. Dietrich In defense of representation Cognitive Psychology 40 2000 138 171
99. D. Marr A theory for cerebral neocortex Proceedings of The Royal Society of London, Series B, Biological Sciences 176 1043 1970 161 234
100. McClelland, J. L.,& Rumelhart, D. E. (1986). A distributed model of human learning and memory. In McClelland, J. L.; Rumelhart, D. E.; the PDP Research Group (Eds). Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 2. Psychological and biological models, pp. 171-215). Cambridge, MA: MIT Press.
101. D.S. McNamara Computational methods to extract meaning from text and advance theories of human cognition Topics in Cognitive Science 3 1 2011 3 17
102. J. Metcalfe Distortions in human memory Michael A. Arbib, The handbook of brain theory and neural networks 1995 MIT Press Cambridge, Massachusetts
103. P.M. Milner The cell assembly: Mark II Psychological Review 64 1957 242 252
104. P.M. Milner Neural representations: Some old problems revisited Journal of Cognitive Neuroscience 8 1996 69 77
105. P.M. Milner The autonomous brain 1999 Laurence Erlbaum Associates New Jersy
106. Y. Miyao, and J. Tsujii Feature forest models for probabilistic HPSG parsing Computational Linguistics 34 1 2008 35 80
107. Y. Miyashita Neuronal correlate of visual associative long-term memory in the primate temporal cortex Nature 335 6193 1988 817 820
108. Murdock, B. B. (1999). The buffer 30 years later: Working memory in a theory of distributed associative memory (TODAM). In Izawa (Ed.). On human memory: Evolution, progress, and reflections on the 30th anniversary of the Atkinson-Shiffrin buffer model (pp. 35-57). Mahwah, NJ: Erlbaum.
109. Murphy, G. L. (2002). The big book of concepts. A Bradford book. The MIT Press Cambridge, Massachusets, London, England.
110. M.V. Nikitin An introduction to linguistic theory of meaning 1988 Moscow Vysshaya Shkola (in Russian)
111. M. Page Connectionist modelling in psychology: A localist manifesto Behavioral and Brain Sciences 23 2000 443 512
112. G. Palm Neural assemblies: An alternative approach to artificial intelligence 1982 Springer Verlag Berlin
113. G. Pezzulo Coordinating with the future: The anticipatory nature of representation Minds and Machines 18 2 2008 179 225
114. T.A. Plate Holographic reduced representations IEEE Transactions on Neural Networks 6 1995 623 641
115. T. Plate Holographic reduced representation: Distributed representation for cognitive structures 2003 CSLI Publications Stanford 300 p
116. T.A. Plate Holographic reduced representations: Convolution algebra for compositional distributed representations J. Mylopoulos, R. Reiter, Proceedings of the 12th international joint conference on artificial intelligence 1991 Morgan Kaufmann San Mateo, CA 30 35
117. J.B. Pollack Recursive distributed representations Artificial Intelligence 46 1990 77 105
118. F. Pulvermuller Words in the brain's language Behavioral and Brain Sciences 22 1999 253 336
119. F. Pulvermüller Brain mechanisms linking language and action Nature Reviews Neuroscience 6 2005 576 582
120. Rachkovskij, D. A. (1990). Development and investigation of multilevel assembly neural networks. PhD dissertation. Kiev, Ukrainian SSR: V. M. Glushkov Institute of Cybernetics (in Russian).
121. D.A. Rachkovskij Representation and processing of structures with binary sparse distributed codes IEEE Transactions on Knowledge and Data Engineering 13 2 2001 261 276 < http://cogprints.org/546/ >
122. D.A. Rachkovskij Some approaches to analogical mapping with structure sensitive distributed representations Journal of Experimental and Theoretical Artificial Intelligence 16 3 2004 125 145
123. Rachkovskij, D. A.; Kussul, E. M. (2000). Building large-scale hierarchical models of the world with binary sparse distributed representations. (Unpublished). < http://cogprints.org/1287 />.
124. D.A. Rachkovskij, and E.M. Kussul Binding and normalization of binary sparse distributed representations by context-dependent thinning Neural Computation 13 2 2001 411 452 < http://cogprints.org/1240/ >
125. D.A. Rachkovskij, I.S. Misuno, and S.V. Slipchenko Randomized projective methods for the construction of binary sparse vector representations Cybernetics and Systems Analysis 48 1 2012 146 156
126. D.A. Rachkovskij, and E.G. Revunova Randomized method for solving discrete ill-posed problems Cybernetics and Systems Analysis 48 4 2012 621 635
127. D.A. Rachkovskij, and S.V. Slipchenko Similarity-based retrieval with structure-sensitive sparse binary distributed representations Computational Intelligence 28 1 2012 106 129
128. D.A. Rachkovskij, S.V. Slipchenko, E.M. Kussul, and T.N. Baidyk Properties of numeric codes for the scheme of random subspaces RSC Cybernetics and Systems Analysis 41 4 2005 509 520
129. D.A. Rachkovskij, S.V. Slipchenko, I.S. Misuno, E.M. Kussul, and T.N. Baidyk Sparse binary distributed encoding of numeric vectors Journal of Automation and Information Sciences 37 11 2005 47 61
130. V.G. Red'ko Evolution, neural networks, intelligence. Models and conceptions of evolutionary cybernetics 2011 URSS Moscow (in Russian)
131. V.G. Red'ko, K.V. Anokhin, M.S. Burtsev, A.I. Manolov, O.P. Mosalov, V.A. Nepomnyashchikh, and D.V. Prokhorov Project Animat Brain: Designing the animat control system on the basis of the functional systems theory M.V. Butz, O. Sigaud, G. Pezzulo, G. Baldassarre, Anticipatory behavior in adaptive learning systems: From brains to individual and social behavior 2007 Springer Verlag Berlin, Heidelberg 94 107 (LNAI 4520)
132. E.G. Revunova, and D.A. Rachkovskij Using randomized algorithms for solving discrete ill-posed problems International Journal Information Theories and Applications 16 2 2009 176 192
133. E.T. Rolls Precis of brain and emotion Behavioral and Brain Sciences 23 2 2000 177 234
134. E.T. Rolls Advantages of dilution in the connectivity of attractor networks in the brain Biologically Inspired Cognitive Architectures 1 2012 44 54
135. E.T. Rolls Invariant visual object and face recognition: neural and computational bases, and a model, VisNet Frontiers in Computational Neuroscience 6 35 2012
136. Sahlgren, M.; Holst, A. & Kanerva, P. (2008). Permutations as a means to encode order in word space. In: Proceedings of the 30th annual meeting of the cognitive science society (CogSci'08), July 23-26, Washington, DC, USA.
137. Samsonovich, A. V. (2009). The constructor metacognitive architecture. In A. V. Samsonovich (Ed.), Biologically inspired cognitive architectures ii: Papers from the AAAI fall symposium. AAAI technical report FS-09-01 (pp. 124-134). Menlo Park, CA: AAAI Press.
138. Samsonovich, A. V. (2010). Toward a unified catalog of implemented cognitive architectures (review). In K. R. Johannsdottir, A. V. Samsonovich, B. Goertzel, & A. Chella (Eds.). Biologically inspired cognitive architectures 2010: Proceedings of the first annual meeting of the bica society. Frontiers in artificial intelligence and applications (Vol. 221, pp. 195-244). Amsterdam, The Netherlands: IOS Press.
139. A.V. Samsonovich On a roadmap for the BICA Challenge Biologically Inspired Cognitive Architectures 1 2012 100 107
140. P.G. Schyns, R.L. Goldstone, and J.P. Thibaut The development of features in object concepts Behavioral and Brain Sciences 21 1 1998 1 54
141. P.G. Schyns, and G.L. Murphy The ontogeny of part representation in object concepts Medin The Psychology of Learning and Motivation Vol. 31 1994 Academic Press San Diego, CA 305 349
142. T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, and T. Poggio Robust object recognition with cortex-like mechanisms IEEE Transactions on Pattern Analysis and Machine Intelligence 29 2007 411 426
143. L. Shastri, and V. Ajjanagadde From simple associations to systematic reasoning: connectionist representation of rules, variables, and dynamic bindings using temporal synchrony Behavioral and Brain Sciences 16 1993 417 494
144. S.V. Slipchenko, and D.A. Rachkovskij Analogical mapping using similarity of binary distributed representations International Journal Information Theories and Applications 16 3 2009 269 290
145. S.V. Slipchenko, D.A. Rachkovskij, and I.S. Misuno Decoding binary distributed representations of numerical vectors Computer Mathematics 3 2005 108 120 (in Russian)
146. P. Smolensky Tensor product variable binding and the representation of symbolic structures in connectionist systems Artificial Intelligence 46 1990 159 216
147. A.M. Sokolov Vector representations for efficient comparison and search for similar strings Cybernetics and Systems Analysis 43 4 2007 484 498
148. B.R. Steunebrink, M. Dastani, and J.-C. Meyer A logic of emotions for intelligent agents Proceedings of the 22nd conference on artificial intelligence (AAAI 2007) 2007 AAAI Press Menlo Park, CA pp. 142-147
149. T. Stewart, and C. Eliasmith Compositionality and biologically plausible models W. Hinzen, M. Werning, E. Machery, Oxford handbook of compositionality 2012 Oxford University Press
150. R. Sun Symbol grounding: A new look at an old idea Philosophical Psychology 13 2 2000 149 172
151. R.S. Sutton, and A.G. Barto Reinforcement learning: An introduction 1998 MIT Press Cambridge, MA (A Bradford Book)
152. Sutton, R. S.; Modayil, J.; Delp, M.; Degris, T.; Pilarski, P. M.; Precup, D. (2011). Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction. In Tumer, Yolum, Sonenberg and Stone (Eds.). Proceedings of the 10th international conference on autonomous agents and multiagent systems (AAMAS 2011, pp. 761-768.). May, 2-6, 2011, Taipei, Taiwan.
153. P. Thagard, K.J. Holyoak, G. Nelson, and D. Gochfeld Analog retrieval by constraint satisfaction Artificial Intelligence 46 1990 259 310
154. S. Thorpe Localized versus distributed representations M.A. Arbib, The handbook of brain theory and neural networks 2003 The MIT Press Cambridge, MA 643 646
155. M.V. Tsodyks Associative memory in neural networks with the Hebbian learning rule Modern Physics Letters B 3 1989 555 560
156. M.V. Tsodyks Hierarchical associative memory in neural networks with low activity level Modern Physics Letters B 4 1990 259 265
157. UNESCO (2012). 100th anniversary of the birth of Mykola (Nikolai) Amosov, scientist (1913-2002). In: Celebration of anniversaries with which UNESCO is associated in 2012-2013, Ukraine. < http://www.unesco.org/new/en/unesco/ events/prizes-and-celebrations/celebrations/anniversaries-celebrated-by-member- states/2013 />.
158. F. van der Velde, and M. de Kamps Neural blackboard architectures of combinatorial structures in cognition Behavioral and Brain Sciences 29 2006 37 108
159. van Gelder, T. (1999) Distributed versus local representation. In MIT Encyclopedia of Cognitive Sciences.
160. C. von der Malsburg Am I thinking assemblies? G. Palm, A. Aertsen, Proceedings of the 1984 trieste meeting on brain theory 1986 Springer-Verlag Heidelberg 161 176
161. D.J. Willshaw, O.P. Buneman, and H.C. Longuet-Higgins Non-holographic associative memory Nature 222 1969 960 962
162. D. Willshaw Holography, associative memory, and inductive generalization G.E. Hinton, J.A. Anderson, Parallel models of associative memory 1981 Lawrence Erlbaum Associates Hillside, NJ 83 104
163. M. Witkowski An action-selection calculus Adaptive Behavior 15 1 2007 73 97