Spiking Neural Networks: Neuron Models, Plasticity, and Graph Applications

SPIKING NEURAL NETWORKS: NEURON MODELS, PLASTICITY, AND GRAPH APPLICATIONS By Shaun Donachy A Thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University. Virginia Commonwealth University, 2015. Director: Krzysztof J. Cios, Professor and Chair, Department of Computer Science Networks of spiking neurons can be used not only for brain modeling but also to solve graph problems (Sala and Cios, 1999). With the use of a computationally efficient Izhikevich neuron model combined with plasticity rules, the networks possess self-organizing characteristics. Two different time-based synaptic plasticity rules are used to adjust weights among nodes in a graph resulting in solutions to graph problems such as finding the shortest path and clustering.

[1]  Soni Chaturvedi,et al.  Image Processing with Spiking Neuron Networks , 2013, Artificial Intelligence, Evolutionary Computing and Metaheuristics.

[2]  Eugene M. Izhikevich,et al.  Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.

[3]  Wulfram Gerstner,et al.  SPIKING NEURON MODELS Single Neurons , Populations , Plasticity , 2002 .

[4]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[5]  L. F Abbott,et al.  Lapicque’s introduction of the integrate-and-fire model neuron (1907) , 1999, Brain Research Bulletin.

[6]  Sander M. Bohte,et al.  Computing with Spiking Neuron Networks , 2012, Handbook of Natural Computing.

[7]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[8]  F. Attneave,et al.  The Organization of Behavior: A Neuropsychological Theory , 1949 .

[9]  Lukasz A. Kurgan,et al.  A new synaptic plasticity rule for networks of spiking neurons , 2006, IEEE Transactions on Neural Networks.

[10]  Andrzej Kasiński,et al.  Comparison of supervised learning methods for spike time coding in spiking neural networks , 2006 .

[11]  Krzysztof J. Cios,et al.  Biological restraint on the Izhikevich neuron model essential for seizure modeling , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).

[12]  Wulfram Gerstner,et al.  Spike-timing dependent plasticity , 2010, Scholarpedia.

[13]  Emile Fiesler,et al.  Neural Network Topologies , 1996 .

[14]  Michal Valko,et al.  Evolutionary Feature Selection for Spiking Neural Network Pattern Classifiers , 2005, 2005 portuguese conference on artificial intelligence.

[15]  Robert A. Legenstein,et al.  What Can a Neuron Learn with Spike-Timing-Dependent Plasticity? , 2005, Neural Computation.

[16]  Ronald J. MacGregor,et al.  Neural and brain modeling , 1987 .

[17]  Lukasz A. Kurgan,et al.  Recognition of Partially Occluded and Rotated Images With a Network of Spiking Neurons , 2010, IEEE Transactions on Neural Networks.

[18]  Nikil D. Dutt,et al.  Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule , 2013, Neural Networks.