Neural processing of counting in evolved spiking and McCulloch-Pitts agents

Artificial Life

Volumn 12, Issue 1, 2006, Pages 1-16

  Saggie Wexler, Keren ^a   Keinan, Alon ^a   Ruppin, Eytan ^a  

^a TEL AVIV UNIVERSITY   (Israel)

Author keywords

Counting; Evolutionary computation; Neurocontroller analysis; Spiking dynamics

Indexed keywords

AUTONOMOUS AGENTS; COMPUTATIONAL METHODS; EVOLUTIONARY ALGORITHMS;

COUNTING; NEURAL PROCESSING; NEUROCONTROLLER ANALYSIS; SPIKING DYNAMICS;

NEURAL NETWORKS;

ANIMAL; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; EVOLUTION; FEEDING BEHAVIOR;

ANIMALS; ARTIFICIAL INTELLIGENCE; EVOLUTION; FEEDING BEHAVIOR; MODELS, NEUROLOGICAL; NEURAL NETWORKS (COMPUTER);

EID: 33645818998     PISSN: 10645462     EISSN: 15309185     Source Type: Journal    
DOI: 10.1162/106454606775186428     Document Type: Article

References (23)

1. Aharonov, R., Segev, L., Meilijson, I., & Ruppin, E. (2003). Localization of function via lesion analysis. Neural Computation, 15, 885-913.
2. Aharonov-Barki, R., Beker, T., & Ruppin, E. (2001). Emergence of memory-driven command neurons in evolved artificial agents. Neural Computation, 13, 691-716.
3. Beer, R. D. (1995). On the dynamics of small continuous-time recurrent neural networks. Adaptive Behavior, 3, 469-509.
4. Beer, R. D. (1996). Toward the evolution of dynamical neural networks for minimally cognitive behavior. In P. Maes, M. Mataric, J. Meyer, J. Pollack, & S. Wilson (Eds.), Proceedings of the 4th International Conference on Simulation of Adaptive Behavior (SAB'96) (pp. 421-429). Cambridge, MA: MIT Press.
5. Bugmann, G. (1997). Biologically plausible neural computation. Biosystems, 40, 11-19.
6. Deese, J., & Hulse, S. (1980). The psychology of learning (5th ed.). New York: McGraw-Hill.
7. Fetterman, J. G. (1993). Numerosity discrimination: Both time and number matter. Journal of Experimental Psychology: Animal Behavior Processes, 19, 149-164.
8. Floreano, D., & Mattiussi, C. (2001). Evolution of spiking neural controllers for autonomous vision-based-robots. In T. Gomi (Ed.), Evolutionary Robotics IV (pp. 38-61). Berlin: Springer-Verlag.
9. Floreano, D., & Mondada, F. (1996). Evolution of homing navigation in a real mobile robot. IEEE Transactions on Systems, Man, and Cybernetics - Part B, 26, 396-407.
10. Gomez, F., & Miikkulainen, R. (1997). Incremental evolution of complex general behavior. Adaptive Behavior, 5, 317-342.
11. Jakobi, N. (1997). Evolutionary robotics and the radical envelope of noise hypothesis. Adaptive Behavior, 6, 325-368.
12. Keinan, A. (2004). Analyzing evolved fault-tolerant neurocontrollers. In Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems (ALife9) (pp. 557-562). Cambridge, MA: MIT Press.
13. Keinan, A., Sandbank, B., Hilgetag, C. C., Meilijson, I., & Ruppin, E. Axiomatic scalable neurocontoller analysis via the Shapley value. Artificial Life (in press).
14. Keinan, A., Sandbank, B., Hilgetag, C. C, Meilijson, I., & Ruppin, E. (2004). Fair attribution of functional contribution in artificial and biological networks. Neural Computation, 16, 1887-1915.
15. Maass, W. (1997). Networks of spiking neurons: The third generation of neural network models. Neural Networks, 10, 1656-1671.
16. Maass, W., & Ruf, B. (1999). On computation with pulses. Information and Computation, 148, 202-218.
17. Marocco, D., & Floreano, D. (2002). Active vision and feature selection in evolutionary behavioral systems. In B. Hallam, D. Floreano, G. Hayes, & J. Meyer (Eds.), From Animals to Animats 7: Proceedings of the Seventh International Conference on Simulation of Adaptive Behavior (SAB'02) (pp. 247-255). Cambridge, MA: MIT Press.
18. Paolo, E. A. D. (2002). Spike timing dependent plasticity for evolved robots. Adaptive Behavior, 10, 243-263.
19. Platt, J. R., & Johnson, D. M. (1971). Localization of position within a homogeneous behavior chain: Effects of error contingencies. Learning and Motivation, 2, 386-414.
20. Ruppin, E. (2002). Evolutionary autonomous agents: A neuroscience perspective. Nature Reviews Neuroscience, 3, 132-141.
21. Scheier, C., Pfeifer, R., & Kunyioshi, Y. (1998). Embedded neural networks: Exploiting constraints. Neural Networks, 11, 1551-1569.
22. Tuci, E., Harvey, I., & Todd, P. M. (2002). Using a net to catch a mate: Evolving CTRNNs for the dowry problem. In B. Hallam, D. Floreano, G. Hayes, & J. Meyer (Eds.), From Animals to Animats 7: Proceedings of the Seventh International Conference on Simulation of Adaptive Behavior (SAB'02) (pp. 292-302). Cambridge, MA: MIT Press.
23. Shapley, L. S. (1953). A value for n-person games. In H. W. Kuhn & A. W. Tucker (Eds.), Contributions to the theory of games, Vol. II (pp. 307-317). Princeton, NJ: Princeton University Press.