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Title: Theories of adaptive neural growth
Author: Joseph, Samuel Russell Hampden
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 1998
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This thesis presents two simulated neural models. The first is a model of neuromuscular development that places an emphasis on achieving biological plausibility. The second is a platform for modifying the connectivity of artificial neural networks. This feature generation mechanism (FGM) platform supports a variety of growth procedures that are inspired by evidence from biological development. The model of development at the mammalian neuromuscular junction (NM) focuses on the achievement of single innervation, and is an extension of the existing dual constraint model (DCM). The identities of the molecules involved in the DCM are proposed and the framework is adjusted accordingly. This extension allows a variety of developmental phenomena to be replicated, including the presence of both activity-dependent & independent competition between terminals. A further framework is established that provides a potential explanation for the paradoxical results of synaptic interaction under focal blockade conditions. The FGM model concerns feed-forward ANNs and attempts to improve their unsupervised pattern recognition ability. Different FGMs consist of functions that in the right combination produce connectivity patterns that maximise the average Shannon information provided by the output of individual nodes. They also allow the network to construct partial input features which form an any-of-N representation of a given input pattern. FGM networks are shown to outperform other straightforward unsupervised ANNs in trials on simple data sets. More demanding tests are performed indicating that an FGM net with Boolean weights outperforms a competitive network using continuous weights. The small superiority of the FGM performance is achieved with a third of the free parameters of the competitive net. The nature of the FGM induced partial connectivity implies that these networks would scale up to larger problems more easily than their fully connected counterparts.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available