Initial results from the use of learning classifier systems to control in vitro neuronal networks

Authors:
Larry Bull;Ivan S. Uroukov
Affiliations:
University of the West of England;University of the West of England
Venue:
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Year:
2007

Citing 7
Cited 3

Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
The Neurally Controlled Animat: Biological Brains Acting with Simulated Bodies

Autonomous Robots
Triggered Rule Discovery in Classifier Systems

Proceedings of the 3rd International Conference on Genetic Algorithms
For real! XCS with continuous-valued inputs

Evolutionary Computation
Applications of Learning Classifier Systems

Applications of Learning Classifier Systems
Classifier fitness based on accuracy

Evolutionary Computation

Computer music meets unconventional computing: Towards sound synthesis with in vitro neuronal networks

Computer Music Journal
Learning classifier systems

Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Experiments with an in-vitro robot brain

Computing with instinct

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we describe the use of a learning classifier system to control the electrical stimulation of cultured neuronal networks. The aim is to manipulate the environment of the cells such that they display elementary learning, i.e., so that they respond to a given input signal in a pre-specified way. Results indicate that this is possible and that the learned stimulation protocols identify seemingly fundamental properties of in vitro neuronal networks.allUse of another learning scheme and simpler stimulation confirms these properties.