Emulation Of Bursting Neurons In Neuromorphic Hardware Based On Phase Change Materials
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Emulation Of Bursting Neurons In Neuromorphic Hardware Based On Phase Change Materials
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Author : Richard Meyes
language : en
Publisher: Anchor Academic Publishing (aap_verlag)
Release Date : 2015
Emulation Of Bursting Neurons In Neuromorphic Hardware Based On Phase Change Materials written by Richard Meyes and has been published by Anchor Academic Publishing (aap_verlag) this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Science categories.
Intro -- CHAPTER 1: Introduction -- CHAPTER 2: A Biological Background -- 2.1. The Neuron -- 2.2. The Synapse -- 2.3. An Overall View -- CHAPTER 3: Experimental Emulations -- 3.1. Modeling STP and LTP in a CMOS Spiking NeuralNetwork Chip -- 3.2. Implementation of STDP based on Phase-ChangeMaterial Synapses -- 3.3. Phase-Change Materials for Artificial NeuralNetworks -- 3.4. An Overall View -- CHAPTER 4: Bursting Neurons -- 4.1. Physiological Mechanisms of Bursting -- 4.2. Bursts as a Unit of Neuronal Information -- 4.3. Bursting for Selective Communication -- 4.4. Modeling Neuronal Bursting Activity -- 4.5. An Overall View -- CHAPTER 5: A PCM Bursting Neuron -- 5.1. Voltage-Controlled Relaxation Oscillation in a PCMDevice -- 5.2. The Analogy to Hippocampal Pyramidal BurstingNeurons -- 5.3. Simulation of a PCM Bursting Neuron -- 5.4. An Overall View -- CHAPTER 6: An Outlook on the Future -- APPENDIX A: Quantification of the MembranePotential -- APPENDIX B: Vocabulary -- List of Figures -- List of Tables -- Bibliography -- Acknowledgement
Emulation Of Bursting Neurons In Neuromorphic Hardware Based On Phase Change Materials
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Author : Richard Meyes
language : en
Publisher: diplom.de
Release Date : 2015-01-01
Emulation Of Bursting Neurons In Neuromorphic Hardware Based On Phase Change Materials written by Richard Meyes and has been published by diplom.de this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-01-01 with Computers categories.
In the history of computing hardware,Moore’s law, named after Intel co-founder Gordon E. Moore, describes a long-termtrend, whereby the number of transistors that can be placed inexpensively on an integrated circuit doubles approximately every two years [1]. Because the number of transistors is crucial for computing performance, significant performance gains could be achieved simply through complementary metal-oxide-semiconductor (CMOS) transistor downscaling. AlthoughMoore’s law, which was mentioned for the first time in 1965, turned out to persist for almost five decades, the nano era poses significant problems to the concept of downscaling [2]. Upon approaching the size of atoms, quantumeffects, such as quantum tunneling, pose fundamental barriers to the trend. Furthermore, the conventional computing paradigm based on the Von-Neumann architecture and binary logic becomes increasingly inefficient considering the growing complexity of todays computational tasks. Hence, new computational paradigms and alternative information processing architectures must be explored to extend the capabilities of future information technology beyond digital logic. A fantastic example for such an alternative information processing architecture is the human brain. The brain provides superior computational features such as ultrahigh density of processing units, low energy consumption per computational event, ultrahigh parallelism in computational execution, extremely flexible plasticity of connections between processing units and fault-tolerant computing provided by a huge number of computational entities. Compared to today’s programmable computers, biological systems are six to nine orders of magnitude more efficient in complex environments [3]. For instance: simulating five seconds of brain activity takes IBM’s state-of-the-art supercomputer Blue Gene a hundred times as long, i.e. 500 s, during which it consumes 1.4 MWof power, whereas the power dissipation in the human central nervous system is of the order of 10W[4, 5]. Thus, it is not only extremely interesting but in terms of computational progress also highly desirable to understand how information is processed in the human brain. The conceptual idea developed within the framework of this thesis tries to contribute to this intention. In contrast to most recent research dealing with the simulation and emulation of specific connections between nerve cells [5–12], the work of this thesis focuses on investigating, on [...]
Spiking Neural Network Learning Benchmarking Programming And Executing
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Author : Guoqi Li
language : en
Publisher: Frontiers Media SA
Release Date : 2020-06-05
Spiking Neural Network Learning Benchmarking Programming And Executing written by Guoqi Li and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-05 with categories.
Memristors For Neuromorphic Circuits And Artificial Intelligence Applications
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Author : Jordi Suñé
language : en
Publisher: MDPI
Release Date : 2020-04-09
Memristors For Neuromorphic Circuits And Artificial Intelligence Applications written by Jordi Suñé and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-09 with Technology & Engineering categories.
Artificial Intelligence (AI) has found many applications in the past decade due to the ever increasing computing power. Artificial Neural Networks are inspired in the brain structure and consist in the interconnection of artificial neurons through artificial synapses. Training these systems requires huge amounts of data and, after the network is trained, it can recognize unforeseen data and provide useful information. The so-called Spiking Neural Networks behave similarly to how the brain functions and are very energy efficient. Up to this moment, both spiking and conventional neural networks have been implemented in software programs running on conventional computing units. However, this approach requires high computing power, a large physical space and is energy inefficient. Thus, there is an increasing interest in developing AI tools directly implemented in hardware. The first hardware demonstrations have been based on CMOS circuits for neurons and specific communication protocols for synapses. However, to further increase training speed and energy efficiency while decreasing system size, the combination of CMOS neurons with memristor synapses is being explored. The memristor is a resistor with memory which behaves similarly to biological synapses. This book explores the state-of-the-art of neuromorphic circuits implementing neural networks with memristors for AI applications.
Electric Double Layer Coupled Oxide Based Neuromorphic Transistors Studies
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Author : Changjin Wan
language : en
Publisher: Springer
Release Date : 2018-12-15
Electric Double Layer Coupled Oxide Based Neuromorphic Transistors Studies written by Changjin Wan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-15 with Technology & Engineering categories.
This book focuses on essential synaptic plasticity emulations and neuromorphic computing applications realized with the aid of three-terminal synaptic devices based on ion-coupled oxide-based electric-double-layer (EDL) transistors. To replicate the robust, plastic and fault-tolerant computational power of the human brain, the emulation of essential synaptic plasticity and computation of neurons/synapse by electronic devices are generally considered to be key steps. The book shows that the formation of an EDL at the dielectric/channel interface that slightly lags behind the stimuli can be attributed to the electrostatic coupling between ions and electrons; this mechanism underlies the emulation of short-term synaptic behaviors. Furthermore, it demonstrates that electrochemical doping/dedoping processes in the semiconducting channel by penetrated ions from electrolyte can be utilized for the emulation of long-term synaptic behaviors. Lastly, it applies these synaptic transistors in an artificial visual system to demonstrate the potential for constructing neuromorphic systems. Accordingly, the book offers a unique resource on understanding the brain-machine interface, brain-like chips, artificial cognitive systems, etc.