COMPLEX NEURAL COMPUTATION WITH SIMPLE DIGITAL NEURONS by Andrew Thomas Nere A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Electrical Engineering) at the UNIVERSITY OF WISCONSIN–MADISON 2013 © Copyright by Andrew Thomas Nere 2013 All Rights Reserved i dedication ii acknowledgments iii table of contents Table of Contents ....................................... iii List of Tables ......................................... ix List of Figures ......................................... x Abstract ............................................xiii 1. Introduction ........................................ 1 1.1 Motivation: Challenges Faced by the von Neumann Architecture .......... 1 1.2 Inspiration: The Cortex as a Computing Model ................... 2 1.3 Spiking Neuron Models and Neuromorphic Hardware ................ 3 1.4 Objectives and Contributions ............................. 4 1.4.1 Computing Capabilities of Simple Spiking Neurons . 4 1.4.2 Identifying Useful Complex Neuronal Dynamics . 4 1.4.3 Modeling the Visual Cortex as a Hierarchical Attractor Network . 5 1.4.4 Visual Cortex Model Applications . 5 1.4.5 Addressing the Neuromorphic Semantic Gap . 5 1.4.6 Automatic Approaches for Deploying Cortical Models on Neuromor- phic Substrates . 6 1.5 Related Published Work ................................ 6 1.6 Dissertation Structure ................................ 7 iv 2. Artificial Neuron Models and Neural Networks ................... 9 2.1 A Brief History of Artificial Neural Networks .................... 9 2.2 Spiking Neuron Models ................................ 12 2.2.1 The Hodgkin-Huxley Model . 14 2.2.2 The Izhikevich Model . 15 2.2.3 The Leaky Integrate and Fire Model . 17 2.3 Biologically Inspired Learning Mechanisms ..................... 20 2.3.1 Hebbian Learning . 20 2.3.2 Spike Timing Dependent Plasticity . 21 2.3.3 Variants of STDP . 22 2.3.4 Reward Based Learning Paradigms . 23 2.4 Introduction to the Cerebral Cortex .......................... 24 2.5 The Visual Cortex ................................... 26 2.6 Recurrent Neural Networks .............................. 30 2.6.1 The Hopfield Attractor Neural Network . 30 2.6.2 Transient and Metastable Attractor Networks . 32 2.6.3 Liquid State Machines . 36 2.6.4 Recurrent Neural Networks Summary . 37 2.7 Summary ....................................... 38 3. Neuromorphic Hardware ................................ 39 3.1 Neurogrid ....................................... 39 v 3.2 The BrainScaleS Neuromorphic Processor ...................... 40 3.3 SpiNNaker ...................................... 42 3.4 IBM’s Neurosynaptic Core .............................. 43 3.4.1 Why Target the Neurosynaptic Core? . 44 3.4.2 Description and Operation of the Neurosynaptic Core . 46 3.4.3 The Neurosynaptic Core with Online Learning . 50 3.5 Summary ....................................... 51 4. Modeling Spiking Neurons and Biologically Inspired Learning Mechanisms . 52 4.1 Leaky Integrate-and-Fire Spiking Neuron Model ................... 52 4.2 Learning with Bursts of Spikes ............................ 54 4.3 Value Dependent Learning .............................. 56 4.4 Homeostatic Renormalization ............................. 57 4.5 Preliminary Spiking Model of the Visual System . 59 4.5.1 Shape Categorization Module . 59 4.5.2 Motion Detection Module . 63 4.5.3 Attention Module . 66 4.5.4 Decision Module and Motor Outputs . 67 4.6 Experimental Results ................................. 67 4.6.1 Experiment 1: Shape Categorization . 68 4.6.2 Experiment 2: Catching Targets and Avoiding Obstacles . 69 vi 4.6.3 Experiment 3: Anticipating a Target Object Location with Multiple Objects . 71 4.7 Summary ....................................... 72 5. Visual Cortex Model ................................... 73 5.1 Extending the LLIF Neuron Model .......................... 73 5.1.1 Short-Term Plasticity . 73 5.1.2 NMDA Modulated Synapses . 76 5.2 The Visual Cortex as a Hierarchical Metastable Attractor . 79 5.2.1 Hierarchical Organization and the Feedforward Pathways . 79 5.2.2 Lateral Connections within Modeled Areas . 82 5.2.3 Invariant Object Recognition Through Feedforward Pathways . 83 5.2.4 Why Attractors? . 84 5.2.5 Integration and Feedback Pathways . 86 5.2.6 Metastability of a Hierarchical Attractor . 87 5.3 Summary ....................................... 91 6. Abilities of a Hierarchical Attractor Network .................... 93 6.1 Pattern Completion and Noise Resilience ....................... 93 6.2 Object Occlusions (Working Memory) ........................ 95 6.3 Access and Routing in a Hierarchical Attractor ................... 97 6.4 Summary .......................................109 vii 7. Deployment on Neuromorphic Substrate . 110 7.1 The Neuromorphic Semantic Gap . 110 7.1.1 Emulating Short-Term Plasticity . 112 7.1.2 Emulating the Long Timescale Effects of NMDA-Mediated Synapses 118 7.1.3 Emulating the Voltage-Dependence of NMDA-mediated Synapses . 120 7.1.4 Emulating Online Learning . 123 7.1.4.1 Emulating Hebbian Learning . 124 7.1.4.2 Emulating STDP Learning . 124 7.1.4.3 Extensions to Learning Assemblies . 128 7.2 Automated Approaches for Neural Network Deployment . 130 7.2.1 Templates for Neuronal Populations . 131 7.2.2 Connecting Populations on Distributed Cores . 133 7.3 Deploying the Visual Cortex Model on Neurosynaptic Cores . 138 7.4 Summary .......................................140 8. Conclusion and Reflections ...............................141 8.1 Summary .......................................141 8.2 Future Work ......................................144 8.2.1 Extending the Visual Cortex Model . 144 8.2.2 Formalizing Large Scale Hierarchical Attractors . 145 8.2.3 Sequential Learning Between Attractor States . 147 8.2.4 Identifying the Appropriate Neuromorphic Primitives . 147 viii 8.3 Reflections .......................................149 8.3.1 Need for Better Neural Programming . 149 8.3.2 Need for Flexible Neuromorphic Substrates . 151 8.3.3 Summary . 154 Bibliography .........................................156 ix list of tables 7.1 Short-term Potentiation Circuit Parameters . 112 7.2 Short-term Depression Circuit Parameters . 116 7.3 NMDA Prolonged Signalling Circuit Parameters . 118 7.4 NMDA Voltage-Dependence Circuit Parameters . 121 7.5 Hebbian Learning Circuit Parameters . 123 7.6 STDP Learning Circuit Parameters . 125 x list of figures 2.1 Multi-Layered Perceptron . 11 2.2 A Biological Neuron . 13 2.3 The Izhikevich Model Behavior . 16 2.4 Leaky Integrate-and-Fire Neuron . 18 2.5 STDP Timing Window . 21 2.6 Two Processing Streams of the Visual Cortex . 26 2.7 Hierarchical Processing in the Visual Cortex . 28 2.8 Hopfield Network Pattern Completion . 31 3.1 IBM’s Neurosynaptic Core . 47 4.1 Simple LLIF Based Visual System . 60 4.2 Motion Detection Circuit . 64 4.3 Simple Shapes Learned by the LLIF Network . 68 4.4 Accuracy of Shape Classification . 69 4.5 Object Tracking Task . 70 4.6 Object Tracking Task with 8% Noise . 70 4.7 Network Performance with Single Object . 71 4.8 Network Performance with Two Objects . 71 5.1 Block Diagram of the Visual Cortex Model . 80 5.2 Formation of a Hierarchical Attractor . 89 xi 5.3 Firing Rates of the Hierarchical Attractor . 91 6.1 Image Reconstruction with Hierarchical Attractor . 94 6.2 Short-term Working Memory of Hierarchical Attractor . 96 6.3 Scaled-down Visual Cortex Model . 100 6.4 Hierarchical Attractor Recognizes There is a Helicopter . 104 6.5 Hierarchical Attractor Recognizes There is no Car . 105 6.6 Hierarchical Attractor Recognizes There is a Green Helicopter on Top . 106 6.7 Hierarchical Attractor Recognizes There is Not a Green Car . 107 7.1 Short-term Potentiation NCN Circuit . 112 7.2 Short-term Potentiation Circuit on Neurosynaptic Core . 114 7.3 Behavior of Short-term Potentiation NCN Circuit . 115 7.4 Short-term Depression NCN Circuit . 116 7.5 Behavior of Short-term Depression NCN Circuit . 117 7.6 NMDA Prolonged Signalling NCN Circuit . 118 7.7 Behavior of NMDA Prolonged Signalling NCN Circuit . 120 7.8 NMDA Voltage-Dependent NCN Circuit . 121 7.9 Hebbian Learning NCN Circuit and Behavior . 123 7.10 STDP Learning NCN Circuit . 125 7.11 STDP Learning NCN Circuit Behavior . 128 7.12 NCN Circuit of Synapse Chain . 129 7.13 Neurosynaptic Core Template Example . 132 xii 7.14 Firing Population Integration Neuron . 134 7.15 Copy Neurons . 135 7.16 Routing Neurons . 137 COMPLEX NEURAL COMPUTATION WITH SIMPLE DIGITAL NEURONS Andrew Thomas Nere Under the supervision of Professor Mikko H. Lipasti At the University of Wisconsin-Madison The desire to understand, simulate, and capture the computational capability of the brain is not a new one. However, in recent years, many advances have been made towards building better models of neurons and cortical networks. Furthermore, a number of high profile projects have proposed, designed, and fabricated neuromorphic substrates inspired by the structure, organization, and behavior of biological brains. This dissertation explores both the software and hardware elements of these neuromor- phic systems. On the software side, this dissertation begins with an exploration of the leaky integrate-and-fire (LIF) spiking neuron, and demonstrates that a network composed of simple LIF neurons is capable of simple object recognition and motion detecting tasks. Furthermore, a number of complex neuronal behaviors which significantly extend the computational power of the LIF neuron are identified. This dissertation proposes that