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.
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References
Maass, W.: Networks of Spiking Neurons: the third generation of neural network models. Neural Networks 10, 1656–1671 (1997)
Bugmann, G.: Biologically Plausible Neural Computation. Biosystems 40, 11–19 (1997)
Maass, W., Ruf, B.: On Computation with pulses. Information and Computation 148(2), 202–218 (1999)
Floreano, D., Mattiussi, C.: Evolution of Spiking Neural Controllers for Autonomous Vision-based Robots, Evolutionary Robotics IV. Springer, Berlin (2001)
Paolo, E.A.D.: Spike-timing dependent plasticity for evolved robot control: neural noise, synchronization and robustness. To appear in Adaptive Behavior 10 (2003)
Aharonov-Barki, R., Beker, T., Ruppin, E.: Emergence of memory-driven command neurons in evolved artificial agents. Neural Computation 13, 691–716 (2001)
Aharonov, R., Segev, L., Meilijson, I., Ruppin, E.: Localization of Function Via Lesion Analysis. Neural Computation 15 (2003)
Keinan, A., Hilgetag, C.C., Meilijson, I.: Fair attribution of contribution: Shapley value analysis of neurocontrollers (2003) (preprint)
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Saggie, K., Keinan, A., Ruppin, E. (2003). Solving a Delayed Response Task with Spiking and McCulloch-Pitts Agents. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds) Advances in Artificial Life. ECAL 2003. Lecture Notes in Computer Science(), vol 2801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39432-7_22
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DOI: https://doi.org/10.1007/978-3-540-39432-7_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20057-4
Online ISBN: 978-3-540-39432-7
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