Solving a delayed response task with spiking and McCulloch-pitts agents

Keren Saggie, Alon Keinan, Eytan Ruppin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationAdvances in Artificial Life
EditorsWolfgang Banzhaf, Jens Ziegler, Thomas Christaller, Peter Dittrich, Jan T. Kim
PublisherSpringer Verlag
Number of pages10
ISBN (Print)3540200576, 9783540200574
StatePublished - 2003
Event7th European Conference on Artificial Life, ECAL 2003 - Dortmund, Germany
Duration: 14 Sep 200317 Sep 2003

Publication series

NameLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
ISSN (Print)0302-9743


Conference7th European Conference on Artificial Life, ECAL 2003


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