Spontaneous evolution of command neurons, place cells and memory mechanisms in autonomous agents

Ranit Aharonov-Barki, Tuvik Beker, Eytan Ruppin

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

2 Scopus citations

Abstract

Using evolutionary simulations, we develop autonomous agents controlled by artificial neural networks (ANNs). In simple life-like tasks of foraging and navigation, high performance levels are attained by agents equipped with fully-recurrent ANN controllers. Examining several experimental settings, differing in the sensory input available to the agents, we find a common structure of a “command neuron” switching the dynamics of the network between radically different behavioural modes. In some of the models the command neuron reflects a map of the environment, acting as a “place cell”. In others it is based on a spontaneously evolving short-term memory mechanism. The resemblance to known findings from neurobiology places Evolved ANNs as an excellent candidate model for the study of structure and function relation in complex nervous systems.

Original languageEnglish
Title of host publicationAdvances in Artificial Life - 5th European Conference, ECAL 1999, Proceedings
EditorsDario Floreano, Jean-Daniel Nicoud, Francesco Mondada
PublisherSpringer Verlag
Pages246-255
Number of pages10
ISBN (Print)3540664521, 9783540664529
DOIs
StatePublished - 1999
Event5th European Conference on Artificial Life, ECAL 1999 - Lausanne, Switzerland
Duration: 13 Sep 199917 Sep 1999

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1674
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th European Conference on Artificial Life, ECAL 1999
Country/TerritorySwitzerland
CityLausanne
Period13/09/9917/09/99

Fingerprint

Dive into the research topics of 'Spontaneous evolution of command neurons, place cells and memory mechanisms in autonomous agents'. Together they form a unique fingerprint.

Cite this