Literatur zum Sommerakademie-Seminar "Information: Währung des Lebens"

Adami, C., (1998). Introduction to Artificial Life. New York: Springer.

Avery, J., (2003). Information Theory and Evolution. Singapore: World Scientific.

Ay, N., Bertschinger, N., Der, R., Güttler, F., and Olbrich, E., (2008). Predictive Information and Explorative Behavior of Autonomous Robots. European Journal of Physics B. Accepted.

Bennett, C. H., (1990). How to Define Complexity in Physics, and Why. In Zurek, W. H., editor, Complexity, Entropy and the Physics of Information, Santa Fe Studies in the Sciences of Complexity, 137-148. Reading, Mass.: Addison-Wesley.

Bennett, C. H., and Landauer, R., (1985). The Fundamental Limits of Computation. Scientific American, 253(1):48-56.

Bergstrom, C. T., and Lachmann, M., (2004). Shannon information and biological fitness. In Information Theory Workshop, 50-54. IEEE.

Bialek, W., Nemenman, I., and Tishby, N., (2001). Predictability, complexity and learning. Neural Computation, 13:2409-2463.

Bradbury, J. W., and Vehrencamp, S. L., (1998). Principles of Animal Communication. Sunderland, Mass.: Sinauer Associates, Inc.

Cover, T. M., and Thomas, J. A., (1991). Elements of Information Theory. New York: Wiley.

Crutchfield, J. P., (1994). The Calculi of Emergence: Computation, Dynamics, and Induction. Physica D, 11-54.

Crutchfield, J. P., and Young, K., (1989). Inferring Statistical Complexity. Physical Review Letters, 63(2):105-108.

Der, R., Hesse, F., and Martius, G., (2006). Rocking Stumper and Jumping Snake from a Dynamical System Approach to Artificial Life. J. Adaptive Behavior, 14(2):105-115.

Der, R., Steinmetz, U., and Pasemann, F., (1999). Homeokinesis - A new principle to back up evolution with learning. In Mohammadian, M., editor, Computational Intelligence for Modelling, Control, and Automation, vol. 55 of Concurrent Systems Engineering Series, 43-47. IOS Press.

Dewar, R., (2003). Information Theory Explanation of the Fluctuation Theorem, Maximum Entropy Production and Self-Organized Criticality in Non-Equilibrium Stationary States. J. Phys. A: Math. Gen., 36(3):631-641.

Dewar, R., (2005). Maximum entropy production and the fluctuation theorem. J. Phys. A: Math. Gen., 38:371-381.

Dusenbery, D. B., (1992). Sensory Ecology. New York: W. H. Freeman and Company.

Eckmann, J. P., and Ruelle, D., (1985). Ergodic theory of chaos and strange attractors. Rev. Mod. Phys., 57(3):617-656.

Friston, K., Kilner, J., and Harrison, L., (2006). A free energy principle for the brain. Journal of Physiology-Paris, 100:70-87.

Kleidon, A., and Lorenz, R. D., editors, (2005). Non-equilibrium Thermodynamics and the Production of Entropy. Springer.

Klyubin, A., Polani, D., and Nehaniv, C., (2007). Representations of Space and Time in the Maximization of Information Flow in the Perception-Action Loop. Neural Computation, 19(9):2387-2432.

Klyubin, A. S., Polani, D., and Nehaniv, C. L., (2004). Organization of the Information Flow in the Perception-Action Loop of Evolved Agents. In Proceedings of 2004 NASA/DoD Conference on Evolvable Hardware, 177-180. IEEE Computer Society.

Klyubin, A. S., Polani, D., and Nehaniv, C. L., (2005a). All Else Being Equal Be Empowered. In Advances in Artificial Life, European Conference on Artificial Life (ECAL 2005), vol. 3630 of LNAI, 744-753. Springer.

Klyubin, A. S., Polani, D., and Nehaniv, C. L., (2005b). Empowerment: A Universal Agent-Centric Measure of Control. In Proc. IEEE Congress on Evolutionary Computation, 2-5 September 2005, Edinburgh, Scotland (CEC 2005), 128-135. IEEE.

Landauer, R., (1961). Irreversibility and Heat Generation in the Computing Process. IBM Journal of Research and Development, 5:183-191.

Laughlin, S. B., (2001). Energy as a constraint on the coding and processing of sensory information. Current Opinion in Neurobiology, 11:475-480.

Laughlin, S. B., Anderson, J. C., Carroll, D. C., and de Ruyter van Steveninck, R. R., (2000). Coding Efficiency and the Metabolic Cost of Sensory and Neural Information. In Baddeley, R., Hancock, P., and Földi\'ak, P., editors, Information Theory and the Brain, 41-61. Cambridge University Press.

Laughlin, S. B., de Ruyter van Steveninck, R. R., and Anderson, J. C., (1998). The metabolic cost of neural information. Nature Neuroscience, 1(1):36-41.

Linsker, R., (1988). Self-Organization in a Perceptual Network. Computer, 21(3):105-117.

Lungarella, M., and Sporns, O., (2006). Mapping Information Flow in Sensorimotor Networks. PLoS Computational Biology, 2(10).

MacKay, D. J. C., (2003). Information Theory, Inference, and Learning Algorithms. Cambridge: Cambridge University Press.

Margulis, L., and Lovelock, J. E., (1974). Biological modulation of the Earth's atmosphere. Icarus, 21(4):471-489.

Martyushev, L. M., and Seleznev, V. D., (2006). Maximum Entropy Production Principle in Physics, Chemistry and Biology. Physics Reports, 426:1-45.

Nadal, J.-P., (2002). Information Theoretic Approach to Neural Coding and Parameter Estimation: A Perspective. In Rao et al., 117-134.

Polani, D., Martinetz, T., and Kim, J., (2001). An Information-Theoretic Approach for the Quantification of Relevance. In Kelemen, J., and Sosik, P., editors, Advances in Artificial Life (Proc. 6th European Conference on Artificial Life), vol. 2159 of LNAI, 704-713. Springer.

Polani, D., Nehaniv, C., Martinetz, T., and Kim, J. T., (2006). Relevant Information in Optimized Persistence vs. Progeny Strategies. In Rocha et al., 337-343.

Prokopenko, M., Gerasimov, V., and Tanev, I., (2006a). Evolving Spatiotemporal Coordination in a Modular Robotic System. In Nolfi, S., Baldassarre, G., Calabretta, R., Hallam, J. C. T., Marocco, D., Meyer, J.-A., Miglino, O., and Parisi, D., editors, From Animals to Animats 9: 9th International Conference on the Simulation of Adaptive Behavior (SAB 2006), Rome, Italy, vol. 4095 of Lecture Notes in Computer Science, 558-569. Berlin, Heidelberg: Springer.

Prokopenko, M., Gerasimov, V., and Tanev, I., (2006b). Measuring Spatiotemporal Coordination in a Modular Robotic System. In Rocha et al., 185-191.

Rao, R. P. N., Olshausen, B. A., and Lewicki, M., editors, (2002). Probabilistic Models of the Brain: Perception and Neural Function. Neural Information Processing Series. A Bradford Book. The MIT Press.

Reichl, L., (1980). A Modern Course in Statistical Physics. Austin: University of Texas Press.

Rieke, F., Warland, D., de Ruyter van Steveninck, R., and Bialek, W., (1999). Spikes. A Bradford Book. MIT Press.

Rocha, L. M., Bedau, M., Floreano, D., Goldstone, R., Vespignani, A., and Yaeger, L., editors, (2006). Proc. Artificial Life X.

Russell, S., and Norvig, P., (2002). Artificial Intelligence: A Modern Approach. Prentice Hall Series in Artificial Intelligence. Prentice Hall. Second edition.

Schrater, P., and Kersten, D., (2002). Vision, Psychophysics and Bayes. In Rao et al., 37-60.

Shalizi, C. R., (2001). Causal Architecture, Complexity and Self-Organization in Time Series and Cellular Automata. PhD thesis, University of Wisconsin-Madison.

Shalizi, C. R., and Crutchfield, J. P., (2002). Information Bottlenecks, Causal States, and Statistical Relevance Bases: How to Represent Relevant Information in Memoryless Transduction. Advances in Complex Systems, 5:1-5.

Sporns, O., and Lungarella, M., (2006). Evolving coordinated behavior by maximizing information structure. In Rocha et al., 323-329.

Taylor, S. F., Tishby, N., and Bialek, W., (2007). Information and Fitness. [q-bio.PE].

Tishby, N., Pereira, F. C., and Bialek, W., (1999). The Information Bottleneck Method. In Proc. 37th Annual Allerton Conference on Communication, Control and Computing, Illinois. Urbana-Champaign.

Vergassola, M., Villermaux, E., and Shraiman, B. I., (2007). 'Infotaxis' as a strategy for searching without gradients. Nature, 445:406-409.

Zhaoping, L., (2006). Theoretical Understanding of the early visual processes by data compression and data selection. Network: Computation in Neural Systems, 17(4):301-334.