References
- Amit D J. Modeling Brain Functions. Cambridge University Press, Cambridge 1989
- Amit D J, Brunel N. Adequate input for learning in attractor neural networks. Network. 1993; 4: 177–94
- Amit D J, Gutfreund H, Sompolinsky H. Information storage in neural networks with low levels of activity. Phys. Rev. A 1987; 35: 2293–303
- Baird B. Nonlinear dynamics of pattern formation and pattern recognition in the rabbit olfactory bulb. Physica D 1986; 22: 150–75
- Beňušková L. Modelling of the effect of the missing fundamental with an attractor neural network. Network: Comput. Neural Syst. 1994; 5: 333–49
- Beňušková L. Modelling transpositional invariancy of melody recognition with an attractor neural network. Network: Comput. Neural Syst. 1995; 6: 313–31
- Biederman I. Recognition by components: a theory of human image understanding. Psychol. Rev. 1987; 94: 115–147
- Bienenstock E, Doursat R. Elastic matching and pattern recognition in neural networks. Neural Networks: From Models to Applications, L Personnaz, G Dreyfus. IDSET, Paris 1989
- Bienenstock E, von der Malsburg C. A neural network for invariant pattern recognition. Europhys. Lett. 1987; 4: 121–6
- Bülthoff H H, Edelman S Y, Tarr M J. How are three-dimensional objects represented in the brain?. AI Memo No 1479. Artificial Intelligence Laboratory, Massachussetts Institute of Technology. 1994
- Černý V. Thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm. J. Optim. Appl. 1985; 45: 41–51
- Clauss G, Ebner H. Gründlagen der Statistik für Psychologen, Pädagogen und Soziologen. Volkseigner, Berlin 1978
- Cooper L A, Shepard R N. Chronometric studies of the rotation of mental images. Visual Information Processing, W G Chase. Academic, New York 1973; 204–47
- Deutsch G, Bourbon W T, Papanicolaou A C, Eisenberg H M. Visuospatial tasks compared via activation of regional cerebral blood flow. Neuropsychology 1988; 26: 445–52
- Dotsenko V S. Neural networks: translation-,rotation- and scale-invariant patterns recognition. J. Phys. A: Math. Gen. 1988; 21: L783–7
- Duda R O, Hart P E. Pattern Classification and Scene Analysis. Wiley, New York 1973
- Edelman S, Bülthoff H H, Sklar E. Task and object learning in visual recognition. AI Memo No 1348. Artificial Intelligence Laboratory, Massachussetts Institute of Technology. 1991
- Edelman S, Weinshall D. A self-organizing multiple-view representation of 3D objects. Biol. Cyb. 1991; 64: 209–19
- Eštok S, Beňušková L. Rotation-invariant pattern recognition with an attractor neural network. Proc. 1st Slovak Neural Net. Symp, P Sinčák. TU Košice, Košice 1996; 41–50
- Faber D S, Korn H. Electrical field effects: their relevance in central neural networks. Physiol. Rev. 1989; 69: 821–63
- Gelperin A, Hopfield J J, Tank D W. The logic of Limax, learning. Model Neural Networks and Behavior, A I Selverston. Plenum, New York 1985
- Georgopoulos A P, Lurito J T, Petrides M, Schwartz A B, Massey J T. Mental rotation of the neuronal population vector. Science 1989; 243: 234–6
- Griniasty M, Tsodyks M, Amit D J. Conversion of temporal correlations between stimuli to spatial correlations between attractors. Neural Computat. 1993; 35: 1–17
- Hopfield J J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl Acad. Sci. USA 1982; 79: 2554–8
- Hummel J E, Biederman I. Dynamic binding in a neural network for shape recognition. Psychol. Rev. 1992; 99: 480–517
- Kandel E R, Schwartz J H, Jessell T M. Principles of Neural Science. Appleton and Lange 3rd edn, Norwalk, MA 1991
- Kirkpatrick S, Gelatt C D, Jr, Vecchi M P. Optimization by simulated annealing. Science 1983; 220: 671–80
- Kosslyn S M. Image and Brain: The Resolution of the Imagery Debate. MIT Press, Cambridge, MA 1995
- Kree R, Zippelius A. Recognition of topological features of graphs and images in neural networks. J. Phys. A: Math. Gen. 1988; 21: L813–8
- Livingstone M, Hubel D. Segregation of form, color, movement, and depth: anatomy, physiology, and perception. Science 1988; 240: 740–9
- Logothetis N K, Pauls J, Poggio T. Viewer-centered object recognition in monkeys. AI Memo No 1473. Artificial Intelligence Laboratory, Massachussetts Institute of Technology. 1994
- Marr D, Nishihara H K. Representation and recognition of the spatial organization of three dimensional structure. Proc. R. Soc. B 1978; 200: 269–94
- Metropolis C, Rosenbluth A W, Rosenbluth M N, Teller A H, Teller E. Equation of state calculations by fast computing machines. J. Chem. Phys. 1953; 21: 1087–92
- Peretto P, Niez J-J. Stochastic dynamics of neural networks. IEEE Trans. Syst. Man Cybern. 1986; 16: 17–83
- Poggio T, Edelman S. A network that learns to recognize three-dimensional objects. Nature 1990; 343: 263–6
- Rolls E T, Treves A, Foster D, Perez-Vicente C. Simulation studies of the hippocampal CA3 subfield modelled as an attractor neural network. Neural Networks 1997; 10: 1559–69
- Shepard R N, Hurwitz S. Upward direction, mental rotation, and discrimination of left and right turns in maps. Cognition 1984; 18: 161–93
- Tarr M J, Pinker S. Mental rotation and orientation-dependence in shape recognition. Cognitive Psychol. 1989; 21: 233–82
- Ullman S. Aligning pictorial descriptions: an approach to object recognition. Cognition 1989; 32: 193–254
- Wallis G. How neurons learn to associate 2D-views in invariant object recognition. Technical Report No 37. 1996, Computational Psychophysics Group, Max-Planck-Institute for Biological Cybernetics