Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.606819
Title: An artificial life perspective on olfactory systems : evolving neural coding, developmental symmetry and odour recognition in agents
Author: Oros, Nicolas Yvan
Awarding Body: University of Hertfordshire
Current Institution: University of Hertfordshire
Date of Award: 2010
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Abstract:
This thesis addresses the problem of creating simulated agents controlled by neural networks that share features with biological olfactory systems. This work draws from the fields of Artificial Life, Artificial Intelligence and Neuroscience. The techniques used in this work included simulated agents and chemicals situated in a 2D environment, spiking neural controllers in which neurons were placed on a 2D substrate and transmission delays depended on the length of the connections, a developmental model used with an indirect encoding that could map a genome onto a neural network, and a genetic algorithm used to evolve controllers. The findings of this program raised several interesting issues. Results have shown that using a biologically plausible sigmoid function to map chemical concentration to the total input current of a leaky integrate-and-fire neuron, agents were able to detect the whole range of chemical concentration as well as small variations. The sensory neurons used in this work are able to encode the stimulus intensity into appropriate firing rates. This research also reveals that two different neural coding strategies can be used by a simple neural network to control an agent. Both temporal coincidence (of spikes) and firing rate encoding strategies were important mechanisms used by the same neural network in different environmental conditions. In addition, realistic model of neural noise were shown to improve the behaviour of an agent to perform a task like chemotaxis. Models used to evolve developmental neural controllers for agents have been created and results have shown that evolved agents could perform a relatively realistic and difficult task, and their neural controllers could encode information in space and time. In this work, the use of symmetrical structures was shown to have major benefits for the evolution of neural controllers. Finally, a detailed analysis of the neural dynamics was conducted on an evolved neural network and has shown that the model generates controllers that use rather sophisticated neural coding strategies involving detailed temporal information. This analysis revealed that single spikes sent at specific moments could modify the whole activity of a network and the behaviour of an agent.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.606819  DOI: Not available
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