Biohybrid Systems: Nerves, Interfaces, and Machines -- Contents -- Preface -- List of Contributors -- 1 Merging Technology with Biology -- 1.1 Introduction -- 1.2 NeuroDesign -- 1.3 The NeuroDesign Approach -- 1.4 Neuromorphic Control of a Powered Orthosis for Crutch-Free Walking -- 1.5 Frontiers of Biohybrid Systems -- 1.6 Chapter Organization -- References -- 2 Principles of Computational Neuroscience -- 2.1 Introduction -- 2.2 Some Physiology of Neurons -- 2.2.1 Membrane Potential -- 2.2.2 Membrane Equivalent Circuit -- 2.2.3 Action Potential: Generation and Propagation -- 2.3 General Formalisms in Neuronal Modeling -- 2.3.1 Conductance-Based Hodgkin-Huxley Model for Action Potential Generation -- 2.3.2 Chemical and Electrical Synaptic Inputs -- 2.3.3 Cable Theory of Neuronal Conduction and Compartmental Modeling -- 2.3.4 Calcium and Calcium-Dependent Potassium Currents -- 2.3.5 Simpli.ed Neuronal Models -- 2.4 Synaptic Coupling and Plasticity -- 2.4.1 Modeling Synaptic Plasticity -- 2.5 Computational Models of Neuronal Systems for Biohybrid Applications -- 2.6 Resources -- References -- 3 Neuromorphic Electronic Design -- 3.1 Choices for Neuromorphic Circuits: Digital versus Analogue -- 3.2 The Breadth of Neuromorphic Systems -- 3.3 The Fundamental Processing Unit: The Neuron -- 3.3.1 Conductance-Based Modeling -- 3.3.2 Compartmental Modeling -- 3.3.2.1 The Dendritic Compartment: Home to the Synapses -- 3.3.2.2 The Somatic Compartment: Spike-Based Processing and the Integrate-and-Fire Model -- 3.3.2.3 The Axonal Compartment: Address-Event Representation -- 3.4 Sensing the Environment -- 3.4.1 Vision -- 3.4.2 The Silicon Retina -- 3.4.3 Audition -- 3.4.3.1 Silicon Cochlea Modeling -- 3.5 Conclusions -- 3.6 Resources -- References -- 4 Principles of Neural Signal Processing -- 4.1 Introduction -- 4.2 Point Process Theory.

4.2.1 De.nition of a Point Process -- 4.2.2 Examples of Point Processes -- 4.2.2.1 The Poisson Process -- 4.2.2.2 Renewal Processes -- 4.2.2.3 Markov Point Processes -- 4.2.2.4 Non-Markovian Point Processes -- 4.2.3 Multiple Point Processes -- 4.3 Analyzing a Point Process -- 4.3.1 The Interval Histogram and Hazard Function -- 4.3.2 The PST Histogram -- 4.3.3 Characterizing Multiple Point Processes -- 4.4 Dynamic Neural Processing -- 4.5 Information Theory and Neural Signal Processing -- 4.5.1 Data Processing Theorem -- 4.5.2 Channel Capacity -- 4.5.3 Rate Distortion Theory -- 4.5.4 Application to Biohybrid Systems -- 4.6 Summary -- References -- 5 Dynamic Clamp in Biomimetic and Biohybrid Living-Hardware Systems -- 5.1 What is a Dynamic Clamp? -- 5.1.1 The Digital Dynamic Clamp -- 5.2 Dynamic Clamp Performance and Limitations -- 5.3 Experimental Applications of Dynamic Clamp -- 5.3.1 Example Application 1: Neuronal Gain Control -- 5.3.1.1 Synaptic Background Noise Mechanism -- 5.3.1.2 Synaptic Depression Mechanism -- 5.3.2 Example Application 2: Constructing Arti.cial Neuronal Circuits -- 5.4 Dynamic Clamp System Implementations and Future -- 5.4.1 Fundamental Considerations -- 5.4.2 Recent and Future Implementations -- 5.5 Resources -- References -- 6 Biohybrid Circuits: Nanotransducers Linking Cells and Neural Electrodes -- 6.1 Introduction to Neural-Electrical Interfaces -- 6.1.1 Typical Types of Microelectrode Arrays -- 6.1.2 Electric Circuit Model -- 6.1.3 Requirements on Electrode Materials -- 6.1.4 Applications of Nanotechnology -- 6.2 Neural Probes with Nanowires -- 6.2.1 Metallic Nanowires for Neural-Electrical Interfaces -- 6.2.2 Metal Oxide Nanowires for Neural-Electrical Interfaces -- 6.3 Microelectrode Arrays with Carbon Nano.bers -- 6.4 Microelectrode Arrays with Carbon Nanotubes.

6.4.1 Microelectrode Arrays with Random Carbon Nanotubes -- 6.4.2 Microelectrode Arrays with Vertically Aligned Carbon Nanotubes -- 6.5 Microelectrode Arrays with Conducting Polymer Nanomaterials -- 6.6 Nanoelectrodes for Neural Probes -- 6.6.1 Metal Nanoelectrodes -- 6.6.2 Carbon Nanotube-Based Nanoelectrodes -- 6.7 Summary and Future Work -- References -- 7 Hybrid Systems Analysis: Real-Time Systems for Design and Prototyping of Neural Interfaces and Prostheses -- 7.1 Introduction -- 7.2 Technology -- 7.2.1 dSPACE Boards -- 7.2.2 Introduction to Programming in Simulink -- 7.2.3 Library for Dynamic Clamp -- 7.3 Applications -- 7.3.1 Building Neuronal Models with Simulink for Real-Time Analysis -- 7.3.2 Propensity to Hazardous Dynamics of the Squid Giant Axon -- 7.4 Hybrid Systems Analysis in the Leech Heart Interneuron -- 7.4.1 Model Heart Interneuron -- 7.4.2 Hybrid Systems Analysis -- 7.5 Discussion -- References -- 8 Biomimetic Adaptive Control Algorithms -- 8.1 Introduction -- 8.1.1 Potential to Enhance Capabilities of Engineered Systems -- 8.1.2 Integrating Engineered Systems with Biological Systems -- 8.1.3 Focus on the Nervous System -- 8.2 Biomimetic Algorithms -- 8.2.1 Input/Output Models -- 8.2.2 Neurostructural Models: Models Based on Regional Neuroanatomy and Neurophysiology -- 8.2.3 Arti.cial Neural Network Models -- 8.2.4 Biophysical Models: Conductance-Based Models and Beyond -- 8.2.5 Central Pattern Generators -- 8.3 Discussion -- 8.4 Future Developments -- References -- 9 Neuromorphic Hardware for Control -- 9.1 Neuromorphic Hardware for Locomotion -- 9.1.1 A Biohybrid System for Restoring Quadrupedal Locomotion -- 9.1.2 Silicon Neural Network Design -- 9.1.3 Using the Chip for Locomotor Control -- 9.2 Neuromorphic Hardware for Audition -- 9.2.1 AER EAR Architecture -- 9.2.2 AER EAR Control Application.

9.3 Neuromorphic Hardware for Vision -- 9.3.1 The Neuromorphic Imager (Silicon Retina) -- 9.3.2 Visual Tracking -- 9.3.3 Object Tracking Application -- 9.4 Conclusions -- References -- 10 Biohybrid Systems for Neurocardiology -- 10.1 Introduction -- 10.2 Autonomic Neural Control of the Heart -- 10.2.1 Antagonistic Neural Control of the Heart -- 10.2.2 Re.exive Neural Control of the Heart -- 10.2.3 Hierarchical Neural Control of the Heart -- 10.3 Monitoring and Modulating the Autonomic Re.exive Control of the Heart -- 10.3.1 Sensing of Afferent Signals from and Efferent Signals to the Heart -- 10.3.2 Stimulation of the Parasympathetic Input to the Heart -- 10.3.2.1 Stimulation of the Cervical Vagus Nerve -- 10.3.2.2 Stimulation of the Autonomic Cardiac Innervation -- 10.3.3 Biohybrid Closed Loop Arti.cial Neural Control of the Heart -- 10.4 Conclusions -- References -- 11 Bioelectronic Sensing of Insulin Demand -- 11.1 Sensor Technologies and Cell Therapy in Diabetes: a Life-Long Debilitating Disease -- 11.2 The Biological Sensor: Function of β-Cells and Islet -- 11.3 Automated Islet Screening and Bioelectronic Sensor of Insulin Demand -- 11.4 Closed Loop Exploration In Vitro -- 11.5 Methods -- 11.5.1 Cultures and MEA -- 11.5.2 Signal Conditioning and Spike Detection -- 11.6 Results -- 11.6.1 Recordings -- 11.6.2 Adaptive Detection -- 11.7 Conclusions -- References -- Index.