Evolution of Associative Learning in Chemical Networks

PLoS Computational Biology

Volumn 8, Issue 11, 2012, Pages

  McGregor, Simon ^a   Vasas, Vera ^b   Husbands, Phil ^a   Fernando, Chrisantha ^c  

^a UNIVERSITY OF SUSSEX   (United Kingdom)

^b UNIVERSITAT AUTÒNOMA DE BARCELONA   (Spain)

^c QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGY; CHEMICAL ANALYSIS;

ASSOCIATIVE LEARNING; CHEMICAL CONCENTRATIONS; CHEMICAL NETWORKS; CHEMICAL SOLUTIONS; IN-SILICO; LEARN+; LEARNING PROBLEM; LINEAR COMBINATIONS; SIMPLE++; SINGLE CELLS;

CELL SIGNALING;

ARTICLE; BAYESIAN LEARNING; COMPUTER MODEL; GENE REGULATORY NETWORK; INTRACELLULAR SIGNALING; LINEAR SYSTEM; MENTAL TASK; NERVE CELL NETWORK; NETWORK LEARNING; PHENOTYPIC VARIATION; REACTION ANALYSIS; STATE DEPENDENT LEARNING; TASK PERFORMANCE;

EID: 84870698219     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1002739     Document Type: Article

References (41)

1. Fernando C, Liekens AML, Bingle LEH, Beck C, Lenser T, et al. (2008) Molecular circuits for associative learning in single-celled organisms. J Roy Soc Interface 6: 463-9.
2. Gandhi N, Ashkenasy G, Tannenbaum E, (2007) Associative Learning in biochemical networks. J Theor Biol 249: 58-66.
3. Magnasco MO, (1997) Chemical kinetics is Turing Universal. Phys Rev Lett 78: 1190-1193.
4. Hjelmfelt A, Weinberger ED, Ross J, (1991) Chemical implementation of neural networks and Turing machines. Proc Natl Acad Sci U S A 88: 10983-10987.
5. Goldstein R, Soyer OS, (2008) Evolution of the Taxis Responses in Virtual Bacteria: Non-Adaptive Dynamics. PLoS Comput Biol 4: e10000084.
6. Parter M, Kashtan N, Alon U, (2008) Facilitated Variation: How Evolution Learns from Past Environments to Generalize to New Environments. PLoS Comput Biol 4: e1000206.
7. Bray D, (2003) Molecular Networks: The Top-Down View. Science 26: 1864-1865.
8. Bray D, Lay S, (1994) Computer simulated evolution of a network of cell-signaling molecules. Biophys J 66: 972-977.
9. Paladugu SR, Chickarmane V, Deckard A, Frumkin JP, McCormack M, et al. (2006) In silico evolution of functional modules in biochemical networks. Syst Biol 153: 223-235.
10. Holland JH (1975) Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press.
11. Fogel DB (2006) Evolutionary Computation: Toward a New Pholosophy of Machine Intelligence. Piscataway, NJ: Wiley-Interscience.
12. Baeck T, Fogel DB, Michalewicz ZM (1997) Handbook of Evolutionary Computation New York: Taylor and Francis Group.
13. Phattanasri P, Chiel HJ, Beer RD, (2007) The dynamics of associative learning in evolved model circuits. Adapt Behav 15: 377-396.
14. Bagley RJ, Farmer JD, Fontana W. (1992) Evolution of a Metabolism. In: Langton CG, Taylor C, Farmer JD, Rasmussen S, editors. Artificial Life II, Proceedings. Santa Fe: Addison-Wesley.
15. Fontana W, Buss LW, (1994) What would be conserved if 'the tape were played twice'? Proc Natl Acad Sci U S A 91: 757-761.
16. Fernando C, Rowe J, (2007) Natural Selection in Chemical Evolution. J Theor Biol 247: 152-167.
17. Fernando C, Rowe J, (2008) The origin of autonomous agents by natural selection. Biosystems 91: 355-373.
18. Vasas V, Fernando CT, Santos M, Kauffman SA, Szathmáry E, (2012) Evolution before genes. Biology Direct 7In press.
19. Dayan P, Abbott L (2001) Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. Cambridge, MA: MIT Press.
20. Mackintosh NJ (1974) The psychology of animal learning. Oxford, England: Academic Press.
21. Rescorla RA, Wagner AR (1972) A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In: Black AH, Prokasy WF, editors. Classical Conditioning II: Current Research and Theory. Appleton-Century-Crofts. pp. 64-99.
22. Kamin LJ (1969) Predictability, surprise, attention and conditioning. In: Campbell BA, Church RM, editors. Punishment and aversive behavior. New York: Appleton-Century-Crofts. pp. 279-296.
23. Gopnik A, Schulz L, (2004) Mechanisms of theory formation in young children. Trends in Cogn Sci 8: 371-377.
24. Barber D (2012) Bayesian Reasoning and Machine Learning. Cambridge University Press.
25. Libby E, Perkins TJ, Swain PS, (2007) Noisy information processing through transcriptional regulation. Proc Natl Acad Sci U S A 104: 7151-7156.
26. Maass W, Markram H, (2004) On the computational power of recurrent circuits of spiking neurons. J Comput Syst Sci 69: 593-616.
27. Maass W, Natschläger T, Markram H, (2002) Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations. Neural Comput 14: 2531-2560.
28. Jaeger H, Maass W, Principe J, (2007) Introduction to the special issue on echo state networks and liquid state machines. Neural Netw 20: 287-289.
29. Bingham E, Mannila H, (2001) Random projection in dimensionality reduction: Applications to image and text data. Data Min Knowl Discov pp. 245-250.
30. Hennessey T, (1979) Classical Conditioning in Paramecia. Anim Learn Behav 7: 419-423.
31. Eckert R, Naitoh Y, Friedman K, (1972) Sensory mechanisms in Paramecium. I. Two cmoponents of the electric response to mechanical stimulation of the anterior surface. J Exp Biol 56: 683-694.
32. Dunlap K, (1977) Localization of Calcium Channels in Paramecium Caudatum. J Physiol 271: 119-133.
33. Gustin MC, Nelson DL, (1987) Regulation of ciliary adenylate cyclase by Ca2+ in Paramecium. Biochem J 246: 337-345.
34. Walters ET, Carew TJ, Kandel ER, (1979) Classical conditioning in Aplysia californica. Proc Natl Acad Sci U S A 76: 6675-6679.
35. Bergström SR, (1968) Induced Avoidance Behaviour in the Protozoa Tetrahymena. Scand J Psychol 9: 215-219.
36. Bergström SR, (1968) Acquisition of an avoidance reaction to light in the protozoa tetrahymena. Scand J Psychol 9: 220-224.
37. Nilsonne G, Appelgren A, Axelsson J, Fredrikson M, Lekander M, (2011) Learning in a simple biological system: a pilot study of classical conditioning of human macrophages in vitro. Behav Brain Funct 7: 47.
38. Tagkopoulos I, Liu Y-C, Tavazoie S, (2008) Predictive Behavior Within Microbial Genetic Networks. Science 320: 1313-1317.
39. Dale K, Collett TS, (2001) Using artificial evolution and selection to model insect navigation. Curr Biol 11: 1305-1316.
40. Mitchell A, Romano GH, Groisman B, Dekel E, Kupiec M, et al. (2009) Adaptive prediction of environmental changes by microorganisms. Nature 460: 220-225.
41. Harvey I (2011) The Microbial Genetic Algorithm. In: Kampis G, editor. ECAL 2009. Budapest, Hungary: Springer, Heidelberg. pp. 126-133.