Use this URL to cite or link to this record in EThOS:
Title: Supervised learning in multilayer spiking neural networks
Author: Sporea, Ioana
Awarding Body: University of Surrey
Current Institution: University of Surrey
Date of Award: 2012
Availability of Full Text:
Access from EThOS:
In this thesis, a new supervised learning algorithm for multilayer spik- ing neural networks is presented. Gradient descent learning algo- rithms have led traditional neural networks with multiple layers to be one of the most powerful and flexible computational models derived from artificial neural networks. However, more recent experimental evidence suggests that biological neural systems use the exact time of single action potentials to encode information. These findings have led to a new way of simulating neural networks based on temporal en- coding with single spikes. Analytical demonstrations show that these types of neural networks are computationally more powerful than net- works of rate neurons. Conversely, the existing learning algorithms no longer apply to spik- ing neural networks. Supervised learning algorithms based on gradient descent, such as SpikeProp and its extensions, have been developed for spiking neural networks with multiple layers, but these ate limited to a specific model of neurons, with only the first spike being consid- ered. Another learning algorithm, ReSuMe, for single layer networks is based on spike-timing dependent plasticity ~STDP) and uses the computational power of multiple spikes; moreover, this algorithm is not limited to a specific neuron model. The algorithm presented here is based on the gradient descent method, while making use of STDP and can be applied to networks with multi- ple layers. Furthermore, the algorithm is not limited to neurons firing single spikes or specific neuron models. Results on classic benchmarks, such as the XOR problem and the Iris data set, show that the algo- rithm is capable of non-linear transformations. Complex classification tasks have also been applied with fast convergence times. The results of the simulations show that the new learning rule is as efficient as SpikeProp while having all the advantages of STDP. The supervised learning algorithm for spiking neurons is compared with the back- propagation algorithm for rate neurons by modelling an audio-visual perceptual illusion, the McGurk effect.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available