Advertisement

Solving a Delayed Response Task with Spiking and McCulloch-Pitts Agents

  • Keren Saggie
  • Alon Keinan
  • Eytan Ruppin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2801)

Abstract

This paper investigates the evolution of evolved autonomous agents that solve a memory-dependent delayed response task. Two types of neurocontrollers are evolved: networks of McCulloch-Pitts neurons, and spiky networks, evolving also the parameterization of the spiking dynamics. We show how the ability of a spiky neuron to accumulate voltage is utilized for the delayed response processing. We further confront new questions about the nature of “spikiness”, showing that the presence of spiking dynamics does not necessarily transcribe to actual spikiness in the network, and identify two distinct properties of spiking dynamics in embedded agents. Our main result is that in tasks possessing memory-dependent dynamics, neurocontrollers with spiking neurons can be less complex and easier to evolve than neurocontrollers employing McCulloch-Pitts neurons. Additionally the combined utilization of spiking dynamics with incremental evolution can lead to the successful evolution of response behavior over very long delay periods.

Keywords

Autonomous Agent Delay Period Success Ratio Counting Process Localization Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Maass, W.: Networks of Spiking Neurons: the third generation of neural network models. Neural Networks 10, 1656–1671 (1997)CrossRefGoogle Scholar
  2. 2.
    Bugmann, G.: Biologically Plausible Neural Computation. Biosystems 40, 11–19 (1997)CrossRefGoogle Scholar
  3. 3.
    Maass, W., Ruf, B.: On Computation with pulses. Information and Computation 148(2), 202–218 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Floreano, D., Mattiussi, C.: Evolution of Spiking Neural Controllers for Autonomous Vision-based Robots, Evolutionary Robotics IV. Springer, Berlin (2001)Google Scholar
  5. 5.
    Paolo, E.A.D.: Spike-timing dependent plasticity for evolved robot control: neural noise, synchronization and robustness. To appear in Adaptive Behavior 10 (2003)Google Scholar
  6. 6.
    Aharonov-Barki, R., Beker, T., Ruppin, E.: Emergence of memory-driven command neurons in evolved artificial agents. Neural Computation 13, 691–716 (2001)zbMATHCrossRefGoogle Scholar
  7. 7.
    Aharonov, R., Segev, L., Meilijson, I., Ruppin, E.: Localization of Function Via Lesion Analysis. Neural Computation 15 (2003)Google Scholar
  8. 8.
    Keinan, A., Hilgetag, C.C., Meilijson, I.: Fair attribution of contribution: Shapley value analysis of neurocontrollers (2003) (preprint)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Keren Saggie
    • 1
  • Alon Keinan
    • 1
  • Eytan Ruppin
    • 1
    • 2
  1. 1.School of Computer SciencesTel-Aviv UniversityTel-AvivIsrael
  2. 2.School of MedicineTel-Aviv UniversityTel-AvivIsrael

Personalised recommendations