Bio-Inspired Information Pathways: From Neuroscience to Neurotronics (Springer Series on Bio- and Neurosystems, 16)

The Springer Series on Bio-and Neurosystems publishes fundamental principles and state-of-the-art research at the intersection of biology, neuroscience, information processing and the engineering sciences. The series covers general informatics methods and techniques, together with their use to answer biological or medical questions. Of interest are both basics and new developments on traditional methods such as machine learning, artificial neural networks, statistical methods, nonlinear dynamics, information processing methods, and image and signal processing. New findings in biology and neuroscience obtained through informatics and engineering methods, topics in systems biology, medicine, neuroscience and ecology, as well as engineering applications such as robotic rehabilitation, health information technologies, and many more, are also examined. The main target group includes informaticians and engineers interested in biology, neuroscience and medicine, as well as biologists and neuroscientists using computational and engineering tools. Volumes published in the series include monographs, edited volumes, and selected conference proceedings. Books purposely devoted to supporting education at the graduate and post-graduate levels in bio-and neuroinformatics, computational biology and neuroscience, systems biology, systems neuroscience and other related areas are of particular interest. Foreword by The Series Editor Preface Contents About the Editors Matter and Mind Matter 1 Introduction 2 The Current State of Information Technology 3 Advanced Computing Architectures and Novel Electronic Devices 3.1 Advanced Computing Architectures 3.2 Novel Electronic Devices 4 Information Processing in Nervous Systems 4.1 Local Aspects of Information Processing in Nervous Systems 4.2 Global Aspects of Information Processing in Nervous Systems 4.3 Phylogenies and Ontogenesis 4.4 Homeostasis 5 Artificial Spatio-temporal Networks 6 Benchmarking for Bio-inspired Computing 7 Discussion 8 Conclusion References Neuromorphic Circuits with Redox-Based Memristive Devices 1 Introduction 2 Requirements for Memristive Devices for Neuromorphic Computing 3 Time-Independent Neural Networks 3.1 Deep Neural Network Implemented in CMOS-Integrated RRAM Arrays Used for Chronic Obstructive Pulmonary Disease Detection 3.2 Stochastic Learning with Binary CMOS-Integrated RRAM Devices 4 Time-Dependent Neural Networks 4.1 Bio-Inspired Learning with Analog Memristive Devices 4.2 Oscillatory Computing References Redox-Based Bi-Layer Metal Oxide Memristive Devices 1 Introduction 2 Interface-Based Devices 2.1 Devices Based on NbxOy/Al2O3 2.2 Devices Based on HfO2/TiOx 3 Filamentary-Based Devices 4 Engineering I-V Characteristics of Memristive Devices 4.1 Engineering of Interface-Based Devices 4.2 Engineering of Filamentary-Based Devices References MemFlash—Floating Gate Transistors as Memristors 1 Introduction 2 The MemFlash Concept 2.1 Functional Principle of a MemFlash Cell 2.2 Physical Device Model of a MemFlash Cell 3 Technology and Scaling Issues of the MemFlash 3.1 Tunneling Oxide Scaling 3.2 Different Types of MemFlash 4 MemFlash Cells for Neuromorphic Computing 4.1 Hebbian Learning Models 5 Conclusion References Critical Discussion of Ex situ and In situ TEM Measurements on Memristive Devices 1 Introduction 2 TEM Sample Preparation 2.1 FIB Preparation of Specific Devices 2.2 Conclusion for the Preparation 3 Spectroscopic Methods 4 Example Characterization of Double Barrier Memristive Devices 5 Invasive TEM Measurements 5.1 Beam Damage Overview 5.2 Thin Film Metal Layers in TEM 5.3 Oxides in the TEM and SEM 5.4 Conclusions of Electron Beam Effects 6 Solutions 6.1 Ex situ Measurements on Horizontal Devices 6.2 FIB Preparation for In situ Experiments 6.3 Conclusions and Best Practice 7 Experimental References Modeling and Simulation of Silver-Based Filamentary Memristive Devices 1 Introduction 2 Electrochemical Metallization Cells 3 Simulation Scenario and Simulation Methods 4 Results and Discussion 5 Conclusions and remarks References Integration of Memristive Devices into a 130 nm CMOS Baseline Technology 1 Introduction 2 General Technological Aspects of Integrated Memristive Devices 2.1 CMOS Baseline Technology Node 2.2 Metal Level Selection in BEOL Interconnects 2.3 CMOS Compatible Memristive Switching Layer 2.4 Optimization of Memristor Module Fabrication Process Steps 3 Conclusion References A Wave Digital Approach Towards Bio-inspired Computing Using Memristive Networks 1 Introduction 2 Digital Emulation Technique 3 Memristive Neuronal Oscillator 4 Stimulus-Driven Topology Formation 4.1 Supervised Topology Formation 4.2 Self-organized Topology Formation 5 An Elementary Decision Problem via Optical Illusions 6 Conclusion References Memristive Switching: From Individual Nanoparticles Towards Complex Nanoparticle Networks 1 Introduction 2 Deep Insight into Single Filament Switching Via an Unconventional cAFM Approach 3 Noble Metal Alloy Nanoparticles for Diffusive Switching 4 Distributed Ag-Based NP Switching in Memristive Networks 4.1 Sparse CNT Networks with Implanted AgAu Nanoparticles 4.2 Nanoparticle Networks 5 Conclusion References Photocatalytic Deposition for Metal Line Formation 1 Introduction 2 Reactive Sputtering of Photocatalytic TiO2 Thin Films 3 Deposition of Metal Structures from Photocatalytic Reduction 4 Metal Line Formation by Photocatalytic Reduction 5 Conclusion References Smart Sensor Arrays 1 Introduction 2 Smart Sensors 3 Additive Manufacturing of Sensors 3.1 Direct Ink Writing of Microparticles 3.2 EtOH/PVB as a Carrier Fluid 3.3 High-Viscosity DIW Setup 3.4 Gas Sensors by DIW 3.5 Enhancing Sensor Properties by Decoration with Noble Metal Nanoparticles 4 Triggered Assembly 5 Conclusion References Bio-inspired, Neuromorphic Acoustic Sensing 1 Introduction 2 Human Hearing/Auditory Pathway 3 Bio-inspired Acoustic Sensing 3.1 Systems with Bio-inspired Pre-processing After the Sensor 3.2 Bio-inspired (Acoustic) Sensors 4 Recently Developed Adaptive, Acoustic Cantilever Sensor 4.1 Frequency Decomposition 4.2 Nonlinear Dynamics 4.3 Adaptation 5 Conclusions References A Bio-inspired Perceptual Decision-Making Circuit Based on the Hassenstein-Reichardt Direction Detector 1 Introduction 2 Materials and Methods 2.1 A Bio-inspired Block Diagram 2.2 The Hassenstein-Reichardt Motion Detector (HRD) 2.3 The Relaxation-Type Oscillator 2.4 LED-Matrix Chaser as a Dot-Task Display 3 Results and Discussion 3.1 Entire Electronic Decision Making System 3.2 Result of Decision-Making 4 Conclusion References Pattern Recognition in the Box Jellyfish Rhopalial Nervous System Mimicked by an Ensemble of Pulsed Coupled Oscillators 1 Introduction 2 The Box Jellyfish: Anatomy, Dynamic and Behavior 2.1 Visual system 2.2 Obstacle Avoidance Behavior 2.3 Pacemaker Activity Represents Behavior 2.4 Lens Eye Morphology and Activity 2.5 Rhopalial Nervous System Organisation 2.6 Retinal Organization, Bipolar Cells Connectivity to ON- and OFF- Ganglion Cells 2.7 Retinal Pre-processing of Basic Shapes (Bars, Contrast Lines, etc.) 2.8 Modeling RNS Visual Information Processing to the Mammalian Retina 3 An Engineered Box Jellyfish by an Ensemble of Pulsed-Coupled Oscillators 4 Conclusion References Biologically Inspired and Energy-Efficient Neurons 1 Introduction 1.1 Memristive Devices 2 Izhikevich-Model Based Low-Power Neuron 2.1 Topology 2.2 Simulation Results 2.3 Measurement Results 3 Ultra-Low-Frequency Hybrid CMOS-Memristive Silicon Neuron 3.1 Ultra-Low-Frequency Relaxation Oscillator 3.2 Simulation and Measurement Results of Relaxation Oscillator 3.3 Experiments of Coupling Systems 4 Conclusion References Synchronization Phenomena in Oscillator Networks: From Kuramoto and Chua to Chemical Oscillators 1 Introduction 2 Multi-clustering in Networks of Phase Oscillators with Dynamic Coupling 2.1 General Nonlinear Model 2.2 Synchronization Invariant Manifolds 2.3 Application to Adaptive Kuramoto Networks 3 Synchronization of Complex Dynamics 3.1 Synchronization of Chaotic Behavior 3.2 Synchronization of Spatiotemporal Patterns 4 Conclusion References Emulation of Learning Behavior in the Hippocampus: From Memristive Learning to Behavioral Tests 1 Introduction 2 Neurobiological Learning Principles 2.1 Cellular Learning Paradigms 2.2 Network Dependent Learning Paradigms 3 Investigating Hippocampal Functions in Animals and Humans 3.1 Mnemonic Similarity Task 3.2 Human Hippocampal Lesion Models 3.3 The Visual Sensory Memory Task (VSMT) 4 Neuromorphic Investigation Pathways 4.1 Hebbian Learning 4.2 Memristive Hebbian Learning 4.3 Emulation of Synaptic Learning 4.4 Emulation of Network Dependent Learning Schemes References