Self-Regulating Neurons. A model for synaptic plasticity in artificial recurrent neural networks

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Title: Self-Regulating Neurons. A model for synaptic plasticity in artificial recurrent neural networks
Authors: Ghazi-Zahedi, Keyan Mahmoud
Thesis referee: Prof. Dr. Pasemann
Prof. Dr. Martin Riedmiller
Abstract: Robustness and adaptivity are important behavioural properties observed in biological systems, which are still widely absent in artificial intelligence applications. Such static or non-plastic artificial systems are limited to their very specific problem domain. This work introducesa general model for synaptic plasticity in embedded artificial recurrent neural networks, which is related to short-term plasticity by synaptic scaling in biological systems. The model is general in the sense that is does not require trigger mechanisms or artificial limitations and it operates on recurrent neural networks of arbitrary structure. A Self-Regulation Neuron is defined as a homeostatic unit which regulates its activity against external disturbances towards a target value by modulation of its incoming and outgoing synapses. Embedded and situated in the sensori-motor loop, a network of these neurons is permanently driven by external stimuli andwill generally not settle at its asymptotically stable state. The system´s behaviour is determinedby the local interactions of the Self-Regulating Neurons. The neuron model is analysed as a dynamical system with respect to its attractor landscape and its transient dynamics. The latter is conducted based on different control structures for obstacle avoidance with increasing structural complexity derived from literature. The result isa controller that shows first traces of adaptivity. Next, two controllers for different tasks are evolved and their transient dynamics are fully analysed. The results of this work not only show that the proposed neuron model enhances the behavioural properties, but also points out the limitations of short-term plasticity which does not account for learning and memory.
Subject Keywords: Recurrent Neural Network; Evolutionary Robotics; Homeostasis
Issue Date: 4-Feb-2009
Type of publication: Dissertation oder Habilitation [doctoralThesis]
Appears in Collections:FB06 - E-Dissertationen

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