I485/H400/I585: Biologically Inspired Computing

Spring 2009

Instructor: Luis M. Rocha, Complex Systems Group, School of Informatics and Cognitive Science Program, Indiana University

Associate Instructor: Artemy Kolchinsky.

Class Location and Time: Monday and Wednesdays, 1:00PM - 2:15PM, Room: Informatics West Building, Room 107

Course Description
Syllabus
Course Evaluation
Life Inspired. The course's blog.
Office Hours
Course Materials
I485 Labs
Oncourse

Course Description

Biological organisms cope with the demands of their environments using solutions quite unlike the traditional human-engineered approaches to problem solving. Biological systems tend to be adaptive, reactive, and distributed. Bio-inspired computing is a field devoted to tackling complex problems using computational methods modeled after design principles encountered in nature. The goal is to produce informatics tools with enhanced robustness, scalability, flexibility and which can interface more effectively with humans. It is a multi-disciplinary field strongly based on Biology, Computer Science, Informatics, Cognitive Science, and robotics. In this course we study bio-inspired algorithms in security, information retrieval, computational intelligence, robotics, modeling and simulation, machine learning, and biology itself.

Aims: Students will be introduced to fundamental topics in bio-inspired computing, and build up their proficiency in the application of various algorithms in real-world problems.

Syllabus

Lecture Outline

What is Life?

What is so cool about life?
Life and Information
The Logical Mechanisms of Life

What is Computation?

What is so cool about computation?
Universal Computation and Computability
Simulations and Realizations

Imitation of Life

Computational Beauty of Nature (fractals, L-systems, chaos)
Bio-inspired computing
Natural computing
Biology through the lens of computer science

Artificial Life and Complex Systems

Self-Organization and Emergent Complex Behavior
Cellular Automata
Development and Morphogenesis
Open-ended evolution

Evolutionary Algorithms

Evolution and Adaptation
Genetic Algorithms
Genetic Programming

Learning

Neurocomputing
learning and evolution: Baldwin effect

Collective Behavior

Social Insects, Stigmergy and Swarm Intelligence
Competition and Cooperation
Communication and Multi-Agent simulation

Immunocomputing

A distributed design for computational intelligence
Engineering Application

Discussion Topics

Evolutionary robots and embodied cognition
Bio-inspired Hardware
Bio-inspired design and problem-solving
Inferring Bio-Networks
Whole organism modeling
Biomolecular Self-Assembly
DNA Computation
Quantum Computation

Course Evaluation

Participation: 15%.

Based upon attendance and participation.

Presentation and Discussion or Assignments: 35%

Undergraduate students (section I485) will complete 5 assignments using algorithms introduced in class. Graduate students (section I585) will present and lead the discussion of an article related to the class materials. This includes presenting concepts necessary to understand the article.

Project or Term Paper: 50%

Depending on background (e.g. CS, informatics, Cog Sci, Psychology) and program (undergraduate, Masters or Phd), students will either tackle a real problem using bio-inspired algorithms, or write a term paper. In either case, students are expected to continuously consult with the instructor regarding the scope and depth of the project or paper.

Office Hours

Luis Rocha

Wednesdays: 10:00am — 12:00pm, 919 10th Street, Room #301

Artemy Kolchinsky

Thursdays: 1:00pm — 3:00pm, 919 10th Street, Room #001

I485 Labs

Jan 21 2009: Introduction to bioinspired algorithms in Python
Feb 11 2009: L-systems
March 4 2009: Cellular Automata & Boolean Networks
April 1 2009: Genetic Algorithms
April 15 2009: Ant Clustering Algorithm

Course Materials

Lecture notes

1. What is Life?
2. The Logical Mechanisms of Life
3. Formalizing and Modeling the World (pdf document)
4. Reality is Stranger than Fiction (pdf document)
5. Self-Organization and Emergent Complex Behavior
6. Von Neumann and Natural Selection (pdf document)
7. Modeling Evolution: Evolutionary Computation

Lecture slides

Printed Resources in OnCourse

Class Handouts

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Aleksander, I. [2002]. "Understanding Information Bit by Bit". In: It must be beautiful : great equations of modern science. G. Farmelo (Ed.), Granta, London.
Bettencourt, L.M.A., J.Lobo, D. Helbing, C. K�hnert, and G.B. West [2007] "Growth, innovation, scaling, and the pace of life in cities.", PNAS 104(17), 7301-7306
C. Christensen, J. Thakar, and R. Albert [2007] "Systems-level insights into cellular regulation: inferring, analyzing and modeling intercellular networks", IET Systems Biology. 1, 61-77.
Crutchfield, J.P. and M. Mitchell [1995]."The evolution of emergent computation." Proc. National Academy of Sciences, USA, Computer Sciences. Vol. 92, pp. 10742-10746.
Dennet, D.C. [2005]. "Show me the Science". New York Times, August 28, 2005
Gershenson, C. (2004). "Introduction to Random Boolean Networks". In Bedau, M., P. Husbands, T. Hutton, S. Kumar, and H. Suzuki (eds.) Workshop and Tutorial Proceedings, Ninth International Conference on the Simulation and Synthesis of Living Systems (ALife IX). pp. 160-173.
Helbing, D., I. Farkas, T. Vicsek[2000]. �Simulating dynamical features of escape panic�. Nature 407, 487-490
Helbing, D., A. Johansson, and H.Z. Al-Abideen [2007]. �The Dynamics of Crowd Disasters: An Empirical Study�. arXiv.org:physics/0701203
T. Helikar, J. Konvalina, J. Heidel, and J.A. Rogers [2008] "Emergent decision-making in biological signal transduction networks.", PNAS. 105(6): 1913-1918
Hinton, G.E. and S.J. Nowlan [1987]."How learning can guide evolution." Complex Systems. Vol. 1, pp.495-502.
Kauffman, S. [1984]. "Emergent Properties In Random Complex Automata". Physica D, 10:145-156
Lindgren, K. [1991]."Evolutionary Phenomena in Simple Dynamics." In: Artificial Life II. Langton et al (Eds). Addison-wesley, pp. 295-312.
Lipson, H. and J. B. Pollack [2000] "Automatic design and Manufacture of Robotic Lifeforms", Nature, 406: 974-978.
Lipson, H. [2005] "Evolutionary Design and Evolutionary Robotics", Biomimetics, CRC Press (Bar Cohen, Ed.) pp. 129-155.
Mitchell, M. [2006] "Complex systems: Network thinking.", Artificial Intelligence. 170,(18):1194�1212
Pattee, H. [1989], "Simulations, Realizations, and Theories of Life". In Artificial Life. C. Langton (Ed.). Addison-Wesley. pp. 63-77.
Pattee, H.H. [1995]. "Summary of most significant points about biological systems".
D. Peak, J.D. West, S.M. Messinger, and K.A. Mott. [2004] "Evidence for complex, collective dynamics and distributed emergent computation in plants.", PNAS. 101(4): 918�922
Ray, T. S. [1992]. "Evolution, ecology and optimization of digital organisms". Santa Fe Institute working paper 92-08-042.
Schmidt, M. and H. Lipson [2009]. "Distilling Free-Form Natural Laws from Experimental Data". Science, 324: 81-85.
Scientific American: Special Issue on the evolution of Evolution, January 2009.
Sims,K. [1994]. "Evolving Virtual Creatures". Proceedings of the 21st annual conference on Computer graphics and interactive techniques, pp. 15 - 22.
J. Timmis, P. Andrews, N. Owens and E. Clark [2008] "An interdisciplinary perspective on artificial immune systems ", Evolutionary Intelligence 1(1), 5-26
Varela, Francisco J.; Maturana, Humberto R.; & Uribe, R. [1974]. �Autopoiesis: the organization of living systems, its characterization and a model�. Biosystems 5 187�196.
Varela, F.J. [1996]. "The early days of autopoiesis". Systems Research, 13(3):407-416.
Varela, F.J. [1997]. "Patterns of Life: Intertwining Identity and Cognition". Brain and Cognition, 34(1):72-87.
Willadsen, K. and Wiles, J. [2007] "Robustness and state-space structure of Boolean gene regulatory models". Journal of Theoretical Biology, 249 (4), 749-765.
Yaeger, L. S. 1994. "Computational Genetics, Physiology, Metabolism, Neural Systems, Learning, Vision, and Behavior or PolyWorld: Life in a New Context. Langton, C. ed. Proceedings of the Artificial Life III Conference. 263-298. Addison-Wesley.