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Table of Contents
Intro
Preface
Acknowledgments
Introduction
Involvement of Same Elements for Memorizing and Processing of Information
Variation of Electrical Properties According to the Hebbian (or Alternative) Rule (Electronic Synapse)
Possibility of Working in Auto-Oscillation Mode
Formation of Stable Chains of the Signal Transfer
Materials Used for Electronic Compounds Must Allow Self-Organisation into 3D Systems Mimicking Intrinsic Brain Functions
Contents
About the Author
Chapter 1: Memristive Devices and Circuits
1.1 Determination of Memristor
1.2 Mnemotrix
1.3 First Mention About the Experimental Realization of Memristor
1.4 Inorganic Memristive Devices
1.5 Memristive Devices with the Organic Materials
Chapter 2: Organic Memristive Device
2.1 Basic Materials
2.2 Structure and Working Principle of the Device
2.3 Electrical Characteristics of the Device
2.4 Device Working Mechanism
2.4.1 Spectroscopy
2.4.2 X-Ray Fluorescence
2.5 Electrical Characteristics in a Pulse Mode
2.6 Optimization of Properties and Stability of the Device
2.6.1 Stability of Organic Memristive Device Properties
2.6.2 Optimization of the Device Architecture
2.6.3 Role of the Electrolyte
2.7 Organic Memristive Devices with Channels, Formed by Layer-by-Layer Technique
Chapter 3: Oscillators Based on Organic Memristive Devices
Chapter 4: Models
4.1 Phenomenological Model
4.2 Simplified Model of the Organic Memristive Device Function
4.3 Electrochemical Model
4.4 Optical Monitoring of the Resistive States
Chapter 5: Logic Elements and Neuron Networks
5.1 Logic Elements with Memory
5.1.1 Element ``OR ́́with Memory
5.1.2 Element ``AND ́́with Memory
5.1.3 Element ``NOT ́́with Memory
5.1.4 Comparison of Logic Elements with Memory, Based on Organic and Inorganic Memristive Devices
5.2 Perceptrons
5.2.1 Single Layer Perceptron
5.2.2 Double Layer Perceptron
Chapter 6: Neuromorphic Systems
6.1 Learning of Circuits Based on a Single Memristive Device
6.1.1 DC Mode
6.1.2 Pulse Mode
6.2 Training of Networks with Several Memristive Elements
6.3 Training Algorithms
6.4 Electronic Analog of the Part of the Nervous System of Pond Snail (Lymnaea Stagnalis)
6.4.1 Biological Benchmark
6.4.2 Experimentally Realized Circuit, Mimicking the Architecture and Properties of the Pond Snail Nervous System Part
6.5 Cross Talk of Memristive Devices During Signal Pathways Formation Process
6.6 Effect of Noise
6.7 Frequency Driven Short-Term Memory and Long-Term Potentiation
6.8 Spike-Timing-Dependent Plasticity (STDP) Learning in Memristive Systems
6.8.1 STDP in Circuits with Polyaniline-Based Memristive Devices
6.8.2 STDP in Circuits with Parylene-Based Memristive Devices
6.8.3 Classic Conditioning of Polyaniline-Based Memristive Devices Systems
Preface
Acknowledgments
Introduction
Involvement of Same Elements for Memorizing and Processing of Information
Variation of Electrical Properties According to the Hebbian (or Alternative) Rule (Electronic Synapse)
Possibility of Working in Auto-Oscillation Mode
Formation of Stable Chains of the Signal Transfer
Materials Used for Electronic Compounds Must Allow Self-Organisation into 3D Systems Mimicking Intrinsic Brain Functions
Contents
About the Author
Chapter 1: Memristive Devices and Circuits
1.1 Determination of Memristor
1.2 Mnemotrix
1.3 First Mention About the Experimental Realization of Memristor
1.4 Inorganic Memristive Devices
1.5 Memristive Devices with the Organic Materials
Chapter 2: Organic Memristive Device
2.1 Basic Materials
2.2 Structure and Working Principle of the Device
2.3 Electrical Characteristics of the Device
2.4 Device Working Mechanism
2.4.1 Spectroscopy
2.4.2 X-Ray Fluorescence
2.5 Electrical Characteristics in a Pulse Mode
2.6 Optimization of Properties and Stability of the Device
2.6.1 Stability of Organic Memristive Device Properties
2.6.2 Optimization of the Device Architecture
2.6.3 Role of the Electrolyte
2.7 Organic Memristive Devices with Channels, Formed by Layer-by-Layer Technique
Chapter 3: Oscillators Based on Organic Memristive Devices
Chapter 4: Models
4.1 Phenomenological Model
4.2 Simplified Model of the Organic Memristive Device Function
4.3 Electrochemical Model
4.4 Optical Monitoring of the Resistive States
Chapter 5: Logic Elements and Neuron Networks
5.1 Logic Elements with Memory
5.1.1 Element ``OR ́́with Memory
5.1.2 Element ``AND ́́with Memory
5.1.3 Element ``NOT ́́with Memory
5.1.4 Comparison of Logic Elements with Memory, Based on Organic and Inorganic Memristive Devices
5.2 Perceptrons
5.2.1 Single Layer Perceptron
5.2.2 Double Layer Perceptron
Chapter 6: Neuromorphic Systems
6.1 Learning of Circuits Based on a Single Memristive Device
6.1.1 DC Mode
6.1.2 Pulse Mode
6.2 Training of Networks with Several Memristive Elements
6.3 Training Algorithms
6.4 Electronic Analog of the Part of the Nervous System of Pond Snail (Lymnaea Stagnalis)
6.4.1 Biological Benchmark
6.4.2 Experimentally Realized Circuit, Mimicking the Architecture and Properties of the Pond Snail Nervous System Part
6.5 Cross Talk of Memristive Devices During Signal Pathways Formation Process
6.6 Effect of Noise
6.7 Frequency Driven Short-Term Memory and Long-Term Potentiation
6.8 Spike-Timing-Dependent Plasticity (STDP) Learning in Memristive Systems
6.8.1 STDP in Circuits with Polyaniline-Based Memristive Devices
6.8.2 STDP in Circuits with Parylene-Based Memristive Devices
6.8.3 Classic Conditioning of Polyaniline-Based Memristive Devices Systems