Next: About this document ...
Up: hoffmann_diss
Previous: E. Notation and Symbols
-
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
[http://www.taygeta.com/Classes.html].
-
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
[http://yann.lecun.com/exdb/mnist/index.html].
-
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
2005-03-22