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Albrecht, S., Busch, J., Kloppenburg, M., Metze, F., and Tavan, P. (2000).
Generalized radial basis function networks for classification and novelty detection: Self-organization of optimal bayesian decision.
Neural Networks, 13, 1075-1093.

Archambeau, C., Lee, J. A., and Verleysen, M. (2003).
On convergence problems of the EM algorithm for finite gaussian mixtures.
In Verleysen, M., (Ed.), Proceedings of the European Symposium on Artificial Neural Networks (ESANN 2003), pages 99-106, Belgium. d-side.

Astafiev, S. V., Stanley, C. M., Shulman, G. L., and Corbetta, M. (2004).
Extrastriate body area in human occipital cortex responds to the performance of motor actions.
Nature Neuroscience, 7, 542-548.

Bach-Y-Rita, P. (1972).
Brain Mechanisms in Sensory Substitution.
Academic Press, New York.

Bachmann, C. M., Cooper, L. N., Dembo, A., and Zeitouni, O. (1987).
A relaxation model for memory with high storage density.
Proceedings of the National Academy of Sciences of the USA, 84, 7529-7531.

Baldi, P. and Heiligenberg, W. (1988).
How sensory maps could enhance resolution through ordered arrangements of broadly tuned receivers.
Biological Cybernetics, 59, 313-318.

Batista, A. P., Buneo, C. A., Snyder, L. H., and Andersen, R. A. (1999).
Reach plans in eye-centered coordinates.
Science, 285, 257-260.

Bishop, C. M. (1995).
Neural Networks for Pattern Recognition.
Oxford University Press, UK.

Blakemore, S. J., Wolpert, D., and Frith, C. (2000).
Why can't you tickle yourself?
NeuroReport, 11, R11-R16.

Blanz, V. and Vetter, T. (1999).
A morphable model for the synthesis of 3D faces.
In Rockwood, A., (Ed.), Siggraph 1999, Computer Graphics Proceedings, pages 187-194, Los Angeles. Addison Wesley Longman.

Blasdel, G. G. and Salama, G. (1986).
Voltage-sensitive dyes reveal a modular organization in monkey striate cortex.
Nature, 321, 579-585.

Brooks, R. A. (1986a).
Achieving artificial intelligence through building robots.
Technical Report A. I. Memo 899, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA.

Brooks, R. A. (1986b).
A robust layered control system for a mobile robot.
IEEE Journal of Robotics and Automation, RA-2, 14-23.

Burges, C. J. C. (1996).
Simplified support vector decision rules.
In Saitta, L., (Ed.), Proceedings of the 13th International Conference on Machine Learning, pages 71-77, San Mateo, CA. Morgan Kaufmann.

Carter Jr., E. F. (1994).
A general purpose simulated annealing class [].

Cipolla, R. and Hollinghurst, N. (1997).
Visually guided grasping in unstructured environments.
Robotics and Autonomous Systems, 19, 337-346.

Colent, C., Pisella, L., Bernieri, C., Rode, G., and Rossetti, Y. (2000).
Cognitive bias induced by visuo-motor adaptation to prisms: A simulation of unilateral neglect in normal individuals.
NeuroReport, 11, 1899-1902.

Cortes, C. and Vapnik, V. (1995).
Support-vector networks.
Machine Learning, 20, 273-297.

Cover, T. M. (1965).
Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition.
IEEE Transactions on Electronic Computers, 14, 326-334.

Cruse, H. (2001).
Building robots with a complex motor system to understand cognition.
In Webb, B. and Consi, T. R., (Eds.), Biorobotics, pages 107-120. MIT Press, Cambridge, MA.

Cruse, H. (2003a).
The evolution of cognition--a hypothesis.
Cognitive Science, 27, 135-155.

Cruse, H. (2003b).
A recurrent network for landmark-based navigation.
Biological Cybernetics, 88, 425-437.

Cruse, H. and Steinkühler, U. (1993).
Solution of the direct and inverse kinematic problems by a common algorithm based on the mean of multiple computations.
Biological Cybernetics, 69, 345-351.

Daszykowski, M., Walczak, B., and Massart, D. L. (2002).
On the optimal partitioning of data with k-means, growing k-means, neural gas, and growing neural gas.
Journal of Chemical Information and Computer Science, 42, 1378-1389.

Dembo, A. and Zeitouni, O. (1988).
General potential surfaces and neural networks.
Physical Review A, 37, 2134-2143.

Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977).
Maximum likelihood from incomplete data via the EM algorithm.
Journal of the Royal Statistical Society. Series B, 39, 1-38.

Diamantaras, K. I. and Kung, S. Y. (1996).
Principal Component Neural Networks.
John Wiley & Sons, New York.

Distante, C., Anglani, A., and Taurisano, F. (2000).
Target reaching by using visual information and Q-learning controllers.
Autonomous Robots, 9, 41-50.

Elman, J. L. (1990).
Finding structure in time.
Cognitive Science, 14, 179-211.

Franz, V. H., Bülthoff, H. H., and Fahle, M. (2003).
Grasp effects of the Ebbinghaus illusion: Obstacle avoidance is not the explanation.
Experimental Brain Research, 149, 470-477.

Fritzke, B. (1995).
A growing neural gas network learns topologies.
Advances in Neural Information Processing Systems, 7, 625-632.

Fuentes, O. and Nelson, R. C. (1998).
Learning dextrous manipulation skills for multifingered robot hands using the evolution strategy.
Machine Learning, 31, 223-237.

Gibson, J. J. (1977).
The theory of affordances.
In Shaw, R. and Bransford, J., (Eds.), Perceiving, Acting, and Knowing, chapter 3, pages 67-82. Erlbaum, Hillsdale, NJ.

Goodale, M. A. and Milner, A. D. (1992).
Separate visual pathways for perception and action.
Trends in Neurosciences, 15, 20-25.

Gordon, I. E. (1989).
Theories of visual perception.
John Wiley & Sons, Chichester, UK.

Graziano, M. S., Taylor, C. S., and Moore, T. (2002).
Complex movements evoked by microstimulation of precentral cortex.
Neuron, 34, 841-851.

Gregory, R. L. (1998).
Eye and Brain, pages 136-169.
Oxford University Press, UK.

Gregory, R. L. (2003).
Seeing after blindness.
Nature Neuroscience, 6, 909-910.

Gross, H.-M., Heinze, A., Seiler, T., and Stephan, V. (1999).
Generative character of perception: A neural architecture for sensorimotor anticipation.
Neural Networks, 12, 1101-1129.

Grush, R. (2004).
The emulation theory of representation: Motor control, imagery, and perception.
Behavioral and Brain Sciences, 27, 377-442.

Harman, K. L., Humphrey, G. K., and Goodale, M. A. (1999).
Active manual control of object views facilitates visual recognition.
Current Biology, 9, 1315-1318.

Hastie, T. and Stuetzle, W. (1989).
Principal curves.
Journal of the American Statistical Association, 84, 502-516.

Haugeland, J. (1986).
Artificial Intelligence: The Very Idea.
MIT Press, Cambridge, MA.

Haykin, S. (1998).
Neural Networks: A Comprehensive Foundation.
Prentice Hall, Paramus, NJ.

Held, R. and Freedman, S. J. (1963).
Plasticity in human sensorimotor control.
Science, 142, 455-462.

Held, R. and Hein, A. (1963).
Movement-produced stimulation in the development of visually guided behaviour.
Journal of Comparative and Physiological Psychology, 56, 872-876.

Hertz, J., Krogh, A., and Palmer, R. G. (1991).
Introduction to the Theory of Neural Computation.
Addison-Wesley, Redwood City, CA.

Hesslow, G. (2002).
Conscious thought as simulation of behaviour and perception.
Trends in Cognitive Sciences, 6, 242-247.

Hinton, G. E., Dayan, P., and Revow, M. (1997).
Modeling the manifolds of images of handwritten digits.
IEEE Transactions on Neural Networks, 8, 65-74.

Hoffmann, H. and Möller, R. (2003).
Unsupervised learning of a kinematic arm model.
In Kaynak, O., Alpaydin, E., Oja, E., and Xu, L., (Eds.), Artificial Neural Networks and Neural Information Processing--ICANN/ICONIP 2003, LNCS, volume 2714, pages 463-470. Springer, Berlin.

Hoffmann, H. and Möller, R. (2004).
Action selection and mental transformation based on a chain of forward models.
In Schaal, S., Ijspeert, A., Billard, A., Vijayakumar, S., Hallam, J., and Meyer, J.-A., (Eds.), From Animals to Animats 8, Proceedings of the Eighth International Conference on the Simulation of Adaptive Behavior, pages 213-222, Los Angeles, CA. MIT Press.

Hopfield, J. J. (1982).
Neural networks and physical systems with emergent collective computational abilities.
Proceedings of the National Academy of Sciences of the USA, 79, 2554-2558.

Hopfield, J. J. (1984).
Neurons with graded response have collective computational properties like those of two-state neurons.
Proceedings of the National Academy of Sciences of the USA, 81, 3088-3092.

Hubel, D. H. and Wiesel, T. N. (1962).
Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.
Journal of Physiology, 160, 106-154.

James, K. H., Humphrey, G. K., Vilis, T., Corrie, B., Baddour, R., and Goodale, M. A. (2002).
``Active'' and ``passive'' learning of three-dimensional object structure within an immersive virtual reality environment.
Behavior Research Methods, Instruments, and Computers, 34, 383-390.

Jeannerod, M. (2001).
Neural simulation of action: A unifying mechanism for motor cognition.
NeuroImage, 14, S103-S109.

Jirenhed, D.-A., Hesslow, G., and Ziemke, T. (2001).
Exploring internal simulation of perception in mobile robots.
Lund University Cognitive Studies, 86, 107-113.

Jordan, M. I. and Rumelhart, D. E. (1992).
Forward models: Supervised learning with a distal teacher.
Cognitive Science, 16, 307-354.

Kambhatla, N. and Leen, T. K. (1997).
Dimension reduction by local principal component analysis.
Neural Computation, 9, 1493-1516.

Kawato, M., Furukawa, K., and Suzuki, R. (1987).
A hierarchical neural-network model for control and learning of voluntary movement.
Biological Cybernetics, 57, 169-185.

Kohonen, T. (1982).
Self-organized formation of topologically correct feature maps.
Biological Cybernetics, 43, 59-69.

Kohonen, T. (1989).
Self-Organization and Associative Memory, 3rd edition.
Springer, Berlin.

Kohonen, T. (1995).
Self-Organizing Maps.
Springer, Berlin.

Kuperstein, M. (1988).
Neural model of adaptive hand-eye coordination for single postures.
Science, 239, 1308-1311.

Kuperstein, M. (1990).
INFANT neural controller for adaptive sensory-motor coordination.
Neural Networks, 4, 131-145.

Latham, P. E., Deneve, S., and Pouget, A. (2003).
Optimal computation with attractor networks.
Journal of Physiology, 97, 683-694.

LeCun, Y. (1998).
The MNIST database of handwritten digits [].

LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998).
Gradient-based learning applied to document recognition.
Proceedings of the IEEE, 86, 2278-2324.

Linde, Y., Buzo, A., and Gray, R. M. (1980).
An algorithm for vector quantizer design.
IEEE Transactions on Communications, 28, 84-95.

Linden, D. E. J., Kallenbach, U., Heinecke, A., Singer, W., and Goebel, R. (1999).
The myth of upright vision. A psychophysical and functional imaging study of adaptation to inverting spectacles.
Perception, 28, 469-481.

Lloyd, S. P. (1982).
Least squares quantization in PCM.
IEEE Transactions on Information Theory, 28, 129-137.

Luria, S. M. and Kinney, J. A. S. (1970).
Underwater vision.
Science, 167, 1454-1461.

Mallot, H. A., Kopecz, J., and von Seelen, W. (1992).
Neuroinformatik als empirische Wissenschaft.
Kognitionswissenschaft, 3, 12-13.

Martinetz, T. M., Berkovich, S. G., and Schulten, K. J. (1993).
``Neural-Gas'' network for vector quantization and its application to time-series prediction.
IEEE Transactions on Neural Networks, 4, 558-569.

Martinetz, T. M. and Schulten, K. J. (1990).
Hierarchical neural net for learning control of a robot's arm and gripper.
In Proceedings of the International Joint Conference on Neural Networks, volume 3, pages 747-752. IEEE, New York.

Meinicke, P. (2000).
Unsupervised Learning in a Generalized Regression Framework.
PhD thesis, Faculty of Technology, Bielefeld University, Germany.

Meinicke, P. and Ritter, H. (2001).
Resolution-based complexity control for gaussian mixture models.
Neural Computation, 13, 453-475.

Micchelli, C. A. (1986).
Interpolation of scattered data: Distance matrices and conditionally positive definite functions.
Constructive Approximation, 2, 11-22.

Mika, S., Schölkopf, B., Smola, A. J., Müller, K.-R., Scholz, M., and Rätsch, G. (1999).
Kernel PCA and de-noising in feature spaces.
Advances in Neural Information Processing Systems, 11, 536-542.

Miller, J. P., Jacobs, G. A., and Theunissen, F. E. (1991).
Representation of sensory information in the cricket cercal sensory system. I. Response properties of the primary interneurons.
Journal of Neurophysiology, 66, 1680-1689.

Molina-Vilaplana, J., Pedreño-Molina, J. L., and López-Coronado, J. (2004).
Hyper RBF model for accurate reaching in redundant robotic systems.
Neurocomputing, 61, 495-501.

Möller, R. (1996).
Wahrnehmung durch Vorhersage--Eine Konzeption der handlungsorientierten Wahrnehmung.
PhD thesis, Faculty of Computer Science and Automation, Ilmenau Technical University, Germany.

Möller, R. (1999).
Perception through anticipation--a behavior-based approach to visual perception.
In Riegler, A., Peschl, M., and von Stein, A., (Eds.), Understanding Representation in the Cognitive Sciences, pages 169-176. Plenum Academic / Kluwer Publishers, New York.

Möller, R. (2002).
Interlocking of learning and orthonormalization in RRLSA.
Neurocomputing, 49, 429-433.

Möller, R. and Hoffmann, H. (2004).
An extension of neural gas to local PCA.
Neurocomputing, 62, 305-326.

Moody, J. and Darken, C. J. (1989).
Fast learning in networks of locally-tuned processing units.
Neural Computation, 1, 281-294.

Movellan, J. R. and McClelland, J. L. (1993).
Learning continuous probability distributions with symmetric diffusion networks.
Cognitive Science, 17, 463-496.

Murata, A., Fadiga, L., Fogassi, L., Gallese, V., Raos, V., and Rizzolatti, G. (1997).
Object representation in the ventral premotor cortex (area F5) of the monkey.
Journal of Neurophysiology, 78, 2226-2230.

Nakazawa, K., Quirk, M. C., Chitwood, R. A., Watanabe, M., Yeckel, M. F., Sun, L. D., Kato, A., Carr, C. A., Johnston, D., Wilson, M. A., and Tonegawa, S. (2002).
Requirement for hippocampal CA3 NMDA receptors in associative memory recall.
Science, 297, 211-218.

Oja, E. (1982).
A simplified neuron model as a principal component analyzer.
Journal of Mathematical Biology, 15, 267-273.

Oja, E. (1989).
Neural networks, principle components, and subspaces.
International Journal of Neural Systems, 1, 61-68.

O'Regan, J. K. and Noë, A. (2001).
A sensorimotor account of vision and visual consciousness.
Behavioral and Brain Sciences, 24, 939-1031.

Ouyang, S., Bao, Z., and Liao, G.-S. (2000).
Robust recursive least squares learning algorithm for principal component analysis.
IEEE Transactions on Neural Networks, 11, 215-221.

Oztop, E., Bradley, N. S., and Arbib, M. A. (2004).
Infant grasp learning: A computational model.
Experimental Brain Research, 158, 480-503.

Parzen, E. (1962).
On estimation of a probability density function and mode.
Annals of Mathematical Statistics, 33, 1065-1076.

Pelah, A. and Barlow, H. B. (1996).
Visual illusion from running.
Nature, 381, 283-283.

Pfeifer, R. and Scheier, C. (1999).
Understanding Intelligence.
MIT Press, Cambridge, MA.

Philipona, D., O'Regan, J. K., and Nadal, J.-P. (2003).
Is there something out there? Inferring space from sensorimotor dependencies.
Neural Computation, 15, 2029-2049.

Philipona, D., O'Regan, J. K., Nadal, J.-P., and Coenen, O. J.-M. D. (2004).
Perception of the structure of the physical world using unknown multimodal sensors and effectors.
In Advances in Neural Information Processing Systems, volume 16. MIT Press.

Pouget, A., Dayan, P., and Zemel, R. S. (2003).
Inference and computation with population codes.
Annual Review of Neuroscience, 26, 381-410.

Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P. (1993).
Numerical Recipes in C: The Art of Scientific Computing.
Cambridge University Press, UK.

Prinz, W. (1997).
Perception and action planning.
European Journal of Cognitive Psychology, 9, 129-154.

Qiu, G., Varley, M. R., and Terrell, T. J. (1994).
Improved clustering using deterministic annealing with a gradient descent technique.
Pattern Recognition Letters, 15, 607-610.

Riedmiller, M. and Braun, H. (1993).
A direct adaptive method for faster backpropagation learning: The RPROP algorithm.
In Proceedings of the IEEE International Conference on Neural Networks, pages 586-591, San Francisco, CA.

Ritter, H., Martinetz, T., and Schulten, K. (1990).
Neuronale Netze.
Addison-Wesley, Bonn, Germany.

Ritter, H. J. (1993).
Parametrized self-organizing maps.
In Gielen, S. and Kappen, B., (Eds.), Proceedings of the International Conference on Artificial Neural Networks, pages 568-575. Springer, Berlin.

Ritter, H. J., Martinetz, T. M., and Schulten, K. J. (1989).
Topology-conserving maps for learning visuo-motor-coordination.
Neural Networks, 2, 159-168.

Ritter, H. J. and Schulten, K. J. (1986).
Topology conserving mappings for learning motor tasks.
In Denker, J. S., (Ed.), Neural Networks for Computing, volume 151, pages 376-380, Snowbird, UT. AIP Conference Proceedings.

Rizzolatti, G., Camarda, R., Fogassi, L., Gentilucci, M., Luppino, G., and Matelli, M. (1988).
Functional organization of inferior area 6 in the macaque monkey.
Experimental Brain Research, 71, 491-507.

Rizzolatti, G. and Fadiga, L. (1998).
Grasping objects and grasping action meanings: The dual role of monkey rostroventral premotor cortex (area F5).
Novartis Foundation Symposium, 218, 81-103.

Rizzolatti, G., Fogassi, L., and Gallese, V. (2001).
Neurophysiological mechanisms underlying the understanding and imitation of action.
Nature Reviews Neuroscience, 2, 661-670.

Rose, K. (1998).
Deterministic annealing for clustering, compression, classification, regression, and related optimization problems.
Proceedings of the IEEE, 86, 2210-2239.

Rose, K., Gurewitz, E., and Fox, G. C. (1990).
Statistical mechanics and phase transitions in clustering.
Physical Review Letters, 65, 945-948.

Rossetti, Y., Rode, G., Pisella, L., Farné, A., Li, L., Boisson, D., and Perenin, M.-T. (1998).
Prism adaptation to a rightward optical deviation rehabilitates left hemispatial neglect.
Nature, 395, 166-169.

Rubner, J. and Tavan, P. (1989).
A self-organizing network for principal-component analysis.
Europhysics Letters, 10, 693-698.

Salganicoff, M., Ungar, L. H., and Bajcsy, R. (1996).
Active learning for vision-based robot grasping.
Machine Learning, 23, 251-278.

Sanger, T. D. (1989).
Optimal unsupervised learning in a single-layer linear feedforward neural network.
Neural Networks, 2, 459-473.

Schenck, W., Hoffmann, H., and Möller, R. (2003).
Learning internal models for eye-hand coordination in reaching and grasping.
In Proceedings of the European Cognitive Science Conference, pages 289-294. Erlbaum, Mahwah, NJ.

Schenck, W. and Möller, R. (2004).
Staged learning of saccadic eye movements with a robot camera head.
In Bowman, H. and Labiouse, C., (Eds.), Connectionist Models of Cognition and Perception II, pages 82-91. World Scientific, London, NJ.

Schölkopf, B., Knirsch, P., Smola, A. J., and Burges, C. (1998a).
Fast approximation of support vector kernel expansions, and an interpretation of clustering as approximation in feature spaces.
In Levi, P., Ahlers, R.-J., May, F., and Schanz, M., (Eds.), 20. DAGM Symposium Mustererkennung, pages 124-132. Springer, Berlin.

Schölkopf, B. and Smola, A. J. (2002).
Learning with Kernels.
MIT Press, Cambridge, MA.

Schölkopf, B., Smola, A. J., and Müller, K.-R. (1998b).
Nonlinear component analysis as a kernel eigenvalue problem.
Neural Computation, 10, 1299-1319.

Simons, D. J. and Wang, R. F. (1998).
Perceiving real-world viewpoint changes.
Psychological Science, 9, 315-320.

Simpson, J. and Weiner, E., (Eds.) (1989).
Oxford English Dictionary, Second Edition.
Oxford University Press, UK.

Steinkühler, U. and Cruse, H. (1998).
A holistic model for an internal representation to control the movement of a manipulator with redundant degrees of freedom.
Biological Cybernetics, 79, 457-466.

Stratton, G. M. (1896).
Some preliminary experiments on vision without inversion of the retinal image.
Psychological Review, 3, 611-617.

Stratton, G. M. (1897).
Vision without inversion of the retinal image.
Psychological Review, 4, 341-360; 463-481.

Sugita, Y. (1996).
Global plasticity in adult visual cortex following reversal of visual input.
Nature, 380, 523-526.

Sun, H.-J., Campos, J. L., and Chan, G. S. W. (2003).
Multisensory integration in the estimation of relative path length.
Experimental Brain Research, 154, 246-254.

Szu, H. and Hartley, R. (1987).
Fast simulated annealing.
Physics Letters A, 122, 157-162.

Tani, J. (1996).
Model-based learning for mobile robot navigation from the dynamical systems perspective.
IEEE Transactions on Systems, Man, and Cybernetics--Part B, 26, 421-436.

Tani, J. and Nolfi, S. (1999).
Learning to perceive the world as articulated: An approach for hierarchical learning in sensory-motor systems.
Neural Networks, 12, 1131-1141.

Tavan, P., Grubmüller, H., and Kühnel, H. (1990).
Self-organization of associative memory and pattern classification: Recurrent signal processing on topological feature maps.
Biological Cybernetics, 64, 95-105.

Tipping, M. E. and Bishop, C. M. (1997).
Probabilistic principal component analysis.
Technical Report 010, Neural Computing Research Group.

Tipping, M. E. and Bishop, C. M. (1999).
Mixtures of probabilistic principal component analyzers.
Neural Computation, 11, 443-482.

Tolman, E. C. (1932).
Purposive Behavior in Animals and Men.
The Century Co., New York.

Treue, S. and Trujillo, J. C. M. (1999).
Feature-based attention influences motion processing gain in macaque visual cortex.
Nature, 399, 575-579.

Uno, Y., Fukumura, N., Suzuki, R., and Kawato, M. (1995).
A computational model for recognizing objects and planning hand shapes in grasping movements.
Neural Networks, 8, 839-851.

Walter, J. A., Nölker, C., and Ritter, H. (2000).
The PSOM algorithm and applications.
In Proceedings of the Symposium on Neural Computation, pages 758-764.

Webb, B. (2001).
Can robots make good models of biological behaviour?
Behavioral and Brain Sciences, 24, 1033-1050.

Wentzell, A. (2003).
Tulane University, Math 301, Lecture 19, Problem 6.

Wexler, M. and Klam, F. (2001).
Movement prediction and movement production.
Journal of Experimental Psychology: Human Perception and Performance, 27, 48-64.

Wohlschläger, A. (2001).
Mental object rotation and the planning of hand movements.
Perception & Psychophysics, 63, 709-718.

Wolpert, D. M., Ghahramani, Z., and Jordan, M. I. (1995).
An internal model for sensorimotor integration.
Science, 269, 1880-1882.

Yair, E., Zeger, K., and Gersho, A. (1992).
Competitive learning and soft competition for vector quantizer design.
IEEE Transactions on Signal Processing, 40, 294-309.

Heiko Hoffmann