|Appears in Collections:||Computing Science and Mathematics eTheses|
|Title:||Synchronization in dynamic neural networks|
|Publisher:||University of Stirling|
|Abstract:||This thesis is concerned with the function and implementation of synchronization in networks of oscillators. Evidence for the existence of synchronization in cortex is reviewed and a suitable architecture for exhibiting synchronization is defined. A number of factors which affect the performance of synchronization in networks of laterally coupled oscillators are investigated. It is shown that altering the strength of the lateral connections between nodes and altering the connective scope of a network can be used to improve synchronization performance. It is also shown that complete connective scope is not required for global synchrony to occur. The effects of noise on synchronization performance are also investigated and it is shown that where an oscillator network is able to synchronize effectively, it will also be robust to a moderate level of noise in the lateral connections. Where a particular oscillator model shows poor synchronization performance, it is shown that noise in the lateral connections is capable of improving synchronization performance. A number of applications of synchronizing oscillator networks are investigated. The use of synchronized oscillations to encode global binding information is investigated and the relationship between the form of grouping obtained and connective scope is discussed. The potential for using learning in synchronizing oscillator networks is illustrated and an investigation is made into the possibility of maintaining multiple phases in a network of synchronizing oscillators. It is concluded from these investigations that it is difficult to maintain multiple phases in the network architecture used throughout this thesis and a modified architecture capable of producing the required behaviour is demonstrated.|
|Type:||Thesis or Dissertation|
This item is protected by original copyright
Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved https://creativecommons.org/publicdomain/zero/1.0/
If you believe that any material held in STORRE infringes copyright, please contact firstname.lastname@example.org providing details and we will remove the Work from public display in STORRE and investigate your claim.