Teaching Assistant: TBA
Class Location and Time: Mondays, 6:15-9:15PM, Engineering Building, Room J01.
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. This course is strongly grounded on the foundations of complex systems and theoretical biology. It aims to provide an understanding of the distributed architectures of natural complex systems, and how those are used to produce computational tools with enhanced robustness, scalability, flexibility and which can interface more effectively with humans. It is a multi-disciplinary field strongly based on biology, complexity, computer science, informatics, cognitive science, robotics, and cybernetics.
Aims: Students are introduced to fundamental topics in evolutionary systems and bio-inspired computing, and build up their proficiency in the application of various algorithms in real-world problems.
Luis Rocha: Thursdays 9 - 11:30am, Engineering Building C3 (Complex Adaptive Systems and Computational Intelligence Lab) or Online, or by appointment
TA: TBA.
| Lab | Assignment |
|---|---|
| Lab 0 |
Python review (No Assignment) |
| Lab 1 | |
| Lab 2 | |
| Lab 3 | |
| Lab 4 |
| Lecture | File & Description |
|---|---|
| Lecture 1 | Introductions and What is Life? |
| Lecture 2 | Life and Information |
| Lecture 3 | Uncertainty-based Information and the Life as Organization |
| Lecture 4 | Modeling the logical Mechanisms and organization of Life Lab 1 - Uncertainty-based Information - Presentation by Shayan Esfarayeni |
| Lecture 5 | Recursion, self-similarity and L-System models |
| Lecture 6 | Dynamical Systems, Attractor Behavior, and Deterministic Chaos |
| Lecture 7 | Automata Networks, Self-Organization and the Criticality Hypothesis |
| Lecture 8 | Criticality and Computation |
| Lecture 9 | Computation in Cellular Automata |
| Lecture 10 | Evolution, Genes, and the threshold of Complexity |
| Lecture 11 | Evolutionary Algorithms |
| Aleksander, I. [2002]. "Understanding Information Bit by Bit". In: It must be beautiful : great equations of modern science. G. Farmelo (Ed.), Grant. |
| Cobb, Matthew. [2013]. "1953: When Genes Became 'Information'." Cell 153 (3): 503-506. |
| Dennet, D.C. [2005]. "Show me the Science". New York Times, August 28, 2005 |
| Gleick, J. [2011]. The Information: A History, a Theory, a Flood. Random House. Chapter 8. |
| Golan, Amos, and John Harte [2022]. "Information theory: A foundation for complexity science". Proceedings of the National Academy of Sciences 119(33): e2119089119 |
| Langton, C. [1989]. "Artificial Life". In Artificial Life. C. Langton (Ed.). Addison-Wesley. pp. 1-47. |
| James, R., and Crutchfield, J. (2017). Multivariate Dependence beyond Shannon Information. Entropy, 19(10), 531. |
| Nunes de Castro, Leandro [2006]. Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications. Chapman & Hall. Chapter 1, pp. 1-23. |
| Pattee, H. [1989], "Simulations, Realizations, and Theories of Life". In Artificial Life. C. Langton (Ed.). Addison-Wesley. pp. 63-77. |
| Prokopenko, Mikhail, Fabio Boschetti, and Alex J. Ryan. "An information theoretic primer on complexity, self organization, and emergence." Complexity 15.1 (2009): 11-28. |
| Polt, R. [2012]. "Anything but Human". New York Times, August 5, 2012 |
| Wigner, E.P. [1960], "The unreasonable effectiveness of mathematics in the natural sciences". Richard courant lecture in mathematical sciences delivered at New York University, May 11, 1959. Comm. Pure Appl. Math., 13: 1-14. |
More to be added as class progresses
| Conrad, M. [1990]. "The geometry of evolution." Biosystems 24: 61-81. |
| Garg, Shivam, Kirankumar Shiragur, Deborah M. Gordon, and Moses Charikar. [2023]."Distributed Algorithms from Arboreal Ants for the Shortest Path Problem." PNAS, 120(6): e2207959120. | Hinton, G.E. and S.J. Nowlan [1987]."How learning can guide evolution." Complex Systems. 1:495-502. |
| Langton, C. [1989]. "Artificial Life". In Artificial Life. C. Langton (Ed.). Addison-Wesley. pp. 1-47. |
| Leemput, Ingrid A van de, Marieke Wichers, Angélique O J Cramer, Denny Borsboom, Francis Tuerlinckx, Peter Kuppens, Egbert H van Nes, et al. "Critical Slowing down as Early Warning for the Onset and Termination of Depression." PNAS 111, no. 1 (January 2014): 87–92. |
| Lindgren, K. [1991]."Evolutionary Phenomena in Simple Dynamics." In: Artificial Life II. Langton et al (Eds). Addison-wesley, pp. 295-312. |
| Manicka, S., M. Marques-Pita, and L.M. Rocha [2022]. "Effective connectivity determines the critical dynamics of biochemical networks" Journal of the Royal Society Interface 19(186): 20210659. |
| Papadopoulou, Marina, Hanno Hildenbrandt, Daniel W. E. Sankey, Steven J. Portugal, and Charlotte K. Hemelrijk. [2022]. "Self-Organization of Collective Escape in Pigeon Flocks" PLOS Computational Biology 18(1): e1009772. |
| Pattee, H. [1989], "Simulations, Realizations, and Theories of Life". In Artificial Life. C. Langton (Ed.). Addison-Wesley. pp. 63-77. |
| Scheffer, Marten, et al. "Early-warning signals for critical transitions." Nature 461.7260 (2009): 53. |
| Schmidt, M. and H. Lipson [2009]. "Distilling Free-Form Natural Laws from Experimental Data". Science, 324: 81-85. |
| More to be added as class progresses |
| Floreano, D. and C. Mattiussi [2008]. Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies. MIT Press. May be available in electronic format for SUNY students. |
Last Modified: April 22, 2025