Emergent Properties of an Arti cial Neural Network

An articial neural network was constructed with the objective of modelling a system with emergent properties. The network was built with biologically derived features. Specically, the neurons were based on the Spike Response Model. Synapses were subject to adaptation by Hebbian learning. The neurons were densely connected locally and organised into columns as found in cortex, and these columns were sparsely connected in the same manner as found in cortex. A simple model of the retina acted as a mechanism to input patterns to the network. A graphical interface to the network was constructed to allow experi-mentation on the properties of synchrony, assembly formation and hierarchy. Synchrony was found to occur among groups of columns, where columns were treated as units in the same way as neurons are normally treated in standard neural networks. The development of hierarchical assemblies was also observed, although in a manner diering from prediction. Observations on assembly behaviour led to the formation of two hypotheses. The rst is that the columnar organisation of neurons may promote synchrony across long distances of cortex. The second is that the hypothesis of formation of columns into hexagonal shaped assemblies may not be valid. Instead, an arbitrary 'chain ' shape is more likely. Acknowledgements I would like to thank my supervisors, Dr. Peter Ross and Dr. Bruce Gra-ham, for the direction given to me throughout the project. Both possessed an uncanny ability to recognise my underlying motivations and interests in the subject matter I chose to study in this project. I would also like to thank my co-workers at my former employers from whom I learned the skills of software development, debugging and project management. Lastly, I would like to acknowledge the innumerable number of column assemblies in the minds of friends and family that in any way contributed to the success of this project. May these assemblies re in perfect synchrony with my own assemblies representing thankfulness. i