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VILNIUS UNIVERSITY Dalius Krunglevičius STDP LEARNING of SPATIAL and SPATIOTEMPORAL PATTERNS Doctoral Dissertation Physical

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VILNIUS UNIVERSITY Dalius Krunglevičius STDP LEARNING of SPATIAL and SPATIOTEMPORAL PATTERNS Doctoral Dissertation Physical VILNIUS UNIVERSITY Dalius Krunglevičius STDP LEARNING OF SPATIAL AND SPATIOTEMPORAL PATTERNS Doctoral Dissertation Physical Sciences, Informatics (09P) Vilnius, 2016 Dissertation work was carried out at the Faculty of Mathematics and Informatics of Vilnius University from 2011 to 2015. Scientific Supervisor Prof. Dr. habil. Šarūnas Raudys (Vilnius University, Physical Sciences, Informatics - 09P) VILNIAUS UNIVERSITETAS Dalius Krunglevičius STDP MOKYMO TAIKYMAS ERDVINĖMS BEI ERDVINĖMS- LAIKINĖMS STRUKTŪROMS ATPAŽINTI Daktaro disertacija Fiziniai mokslai, informatika (09P) Vilnius, 2016 Disertacija rengta 2011-2015 metais Vilniaus universiteto Matematikos ir informatikos fakultete. Mokslinis vadovas prof. habil. dr. Šarūnas Raudys (Vilniaus universitetas, fiziniai mokslai, informatika - 09P) Acknowledgements I’m sincerely grateful to professor Šarunas Raudys for his support, help and belief in the significance of my work. I would like to thank professor Adam Czajka for providing me with data-sets for my experiments and Geoff Vasil for helping with the editing. And my deepest gratitude goes to my wife, Gražina, without whose patience and support this dissertation would have never been written. 5 Table of Contents Notation .............................................................................................................. 9 Introduction ....................................................................................................... 10 Motivation and the Field of Research ........................................................... 10 Objectives of Research and Problems ...................................................... 12 Relevance .................................................................................................. 14 Practical Value of the Research ................................................................ 14 Aim of the Research ................................................................................. 14 Tasks of the Research ............................................................................... 14 Methods .................................................................................................... 15 Scientific Novelty ..................................................................................... 15 Thesis statements ...................................................................................... 16 Approbation .............................................................................................. 16 Structure of the Dissertation ..................................................................... 17 1. The Physiology of the Neuron and Synaptic Plasticity ............................ 19 1.1. The Spike ............................................................................................. 19 1.2. Postsynaptic Potentials and Synapses ................................................. 23 1.3. Synaptic Plasticity ............................................................................... 25 1.4. Spike-Timing Dependent Plasticity .................................................... 27 1.5. Neural Coding ..................................................................................... 30 1.5.1. Rate Coding ............................................................................... 31 1.5.2. Temporal Coding ...................................................................... 32 1.5.3. Phase-of-Firing Code ................................................................ 33 1.5.4. Population Coding..................................................................... 34 2. Phenomenological Models of Spiking Neuron and Synaptic Plasticity ... 35 2.1. Spiking Neuron Models ...................................................................... 35 6 2.1.1. Hodgkin-Huxley Model ............................................................ 35 2.1.2. The Leaky Integrate-and-Fire Model ........................................ 39 2.1.3. Spike Response Model .............................................................. 42 2.1.4. Compartmental Models ............................................................. 45 2.1.5. Other Models of the Spiking Neuron ........................................ 46 2.2. Phenomenological Models of STDP ................................................... 46 2.2.1. Basic STDP Implementation ..................................................... 47 2.2.2. Online Implementation of STDP .............................................. 48 2.2.3. Multiplicative Update vs. Additive Update .............................. 49 2.2.4. All-to-All vs. Nearest-Neighbor Spike Interaction ................... 51 2.2.5. The Triplet Rule ........................................................................ 53 3. Results of Research ................................................................................... 55 3.1. Neural Processing of Long-Lasting Sequences of Temporal Codes .. 55 3.1.1. The Network Model .................................................................. 58 3.1.2. Materials and Methods .............................................................. 62 3.1.3. Parameters of the Simulation .................................................... 65 3.1.4. Training Samples ...................................................................... 67 3.1.5. Learning Conditions in Layer L5 .............................................. 67 3.1.6. Results ....................................................................................... 68 3.1.7. Discussion ................................................................................. 71 3.2. STDP Learning under Variable Noise Levels ..................................... 74 3.2.1. Some Properties of the Inverted STDP Rule ............................ 74 3.2.2. Materials and Methods .............................................................. 77 3.2.3. Results ....................................................................................... 78 3.2.4. Discussion ................................................................................. 82 7 3.3. Competitive STDP Learning of Overlapping Spatial Patterns ........... 84 3.3.1. STDP Learning Success Dependency on Quantity of Stimulation and Synaptic Strength Factor .................................................................... 86 3.3.2. The Network Circuit ................................................................. 87 3.3.3. Results ....................................................................................... 89 3.3.4. Discussion ................................................................................. 94 3.4. Some Properties of the Postsynaptic Process of the SRM Neuron ..... 96 3.4.1. The Normality of the Process of Postsynaptic Membrane Potential 96 3.4.2. Distribution of the Latencies of Postsynaptic Spikes in the Case of Absence of Afterhyperpolarization ...................................................... 98 3.4.3. Discussion ............................................................................... 105 3.5. Modified STDP Triplet Rule Significantly Increases Neuron Training Stability in the Learning of Spatial Patterns ............................................... 106 3.5.1. Materials and Methods ............................................................ 108 3.5.2. Results ..................................................................................... 113 3.5.3. Discussion ............................................................................... 123 Conclusions ..................................................................................................... 127 References ....................................................................................................... 129 8 Notation ANN – artificial neural network. STDP – spike-timing-dependent plasticity. LTD – long term depression. LTP – long term potentiation. PSP – postsynaptic potential. EPSP – excitatory postsynaptic potential. IPSP – inhibitory postsynaptic potential. SRM – spike response model. PDF – probability distribution function. WTA – winner-take-all. ϑ – neuron threshold. w – synaptic strength/weight. – synaptic strength factor. u – membrane potential. ϵ – SRM response kernel function which describes the response over time to an incoming spike. – response kernel function which describes the action potential and after-hyperpolarization. mean. 9 Introduction Motivation and the Field of Research The large goal in the scientific field of machine learning is to achieve human- level cognition and eventually artificial intelligence, a thinking machine capable of human-level reasoning and beyond. This aspiration remains for now largely in the realm of science fiction, but it is the direction of and motivation for continuing scientific research. Spin-offs from these efforts include a large variety of machines which are applicable to the practical tasks of heuristic optimization, pattern recognition, prediction, data clustering, dimensionality reduction and other jobs. Machine learning is applied in multiple fields of human endeavor, from predicting financial trends to medical diagnosis, from entertainment to industrial engineering. There are multiple approaches for building systems which are capable of learning, and most of them are based on mimicking nature, such as Darwinian evolution, the behavior of swarms, etc. In the emulation of human-level cognition, there are of course attempts to mimic the behavior of the central nervous system. There are two distinct
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