Emerging Technologies and Systems for Biologically Plausible Implementations of Neural Functions PDF Download

Author: Erika Covi
Publisher: Frontiers Media SA
ISBN: 2889760006
Category : Science
Languages : en
Pages : 244

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Author: Farnood Merrikh Bayat
Publisher:
ISBN: 9781339084589
Category :
Languages : en
Pages : 157

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Book Description
Nowadays with unbounded expansion of digital world, powerful information processing systems governed by deep learning algorithms are becoming more and more popular. In this situation, usage of fast, powerful, intelligent and trainable deep learning methods seems critical and unavoidable. However, despite of their inherent structural and conceptual differences, all of these intelligent methods and systems share one common property i.e. having enormous number of trainable parameters. However, from a hardware point of view, the size of a practical computing system is always determined based on available resources. In this dissertation, we study these deep learning methods from a hardware point of view and investigate the possibility of their hardware implementation based on two new emerging technologies i.e. resistive switching and floating gate (flash) devices. For this purpose, memristive devices are fabricated with high density in crossbar structure to create a network which then trained with modified RPROB rule to successfully classify images. In addition, biologically plausible spike-timing dependent plasticity rule and its dependence to initial state is demonstrated experimentally on these nano-scale devices. Similar procedure is followed for the other technology, i.e. flash devices. We modified and fabricated the conventional design of digital flash memories which provide us with the ability of individual programming of floating-gate transistors. Having large-scale neural networks in mind, an efficient and high speed tuning method is developed based on acquired dynamic and static models which are then tested experimentally on commercial devices. We have also experimentally investigated the possibility of implementing vector-to-matrix multiplier using these devices which is the main building block of most deep learning methods. Finally, a multi-layer neural network is designed and fabricated using this technology to classify handwritten digits.

Author: Fouad Sabry
Publisher: One Billion Knowledgeable
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 471

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What Is Neuromorphic Engineering Neuromorphic computing and neuromorphic engineering are both terms that refer to the same thing: the use of very-large-scale integration (VLSI) systems that incorporate electrical analog circuits to simulate neuro-biological structures that are found in the nervous system. Any electronic device that does calculations with the help of artificial neurons that are implemented as physical structures is referred to as a neuromorphic computer or chip. Recently, the word "neuromorphic" has been used to refer to analog, digital, mixed-mode analog/digital VLSI, and software systems that embody models of brain systems. This use of the term has become more common. To actualize the implementation of neuromorphic computing on the hardware level, oxide-based memristors, spintronic memory, threshold switches, and transistors are some of the components that may be used. Training software-based neuromorphic systems of spiking neural networks can be accomplished through the use of error backpropagation, for instance through the utilization of Python-based frameworks like snnTorch, or through the utilization of canonical learning rules from the biological learning literature, for instance through the utilization of BindsNet. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Neuromorphic engineering Chapter 2: Artificial neuron Chapter 3: Bio-inspired computing Chapter 4: Steve Furber Chapter 5: Carver Mead Chapter 6: Recurrent neural network Chapter 7: Neural network Chapter 8: Wetware computer Chapter 9: Computational neurogenetic modeling Chapter 10: Spiking neural network Chapter 11: Neurorobotics Chapter 12: Misha Mahowald Chapter 13: Memristor Chapter 14: Physical neural network Chapter 15: NOMFET Chapter 16: Massimiliano Versace Chapter 17: Kwabena Boahen Chapter 18: SpiNNaker Chapter 19: Cognitive computer Chapter 20: Glossary of artificial intelligence Chapter 21: Hai Li (II) Answering the public top questions about neuromorphic engineering. (III) Real world examples for the usage of neuromorphic engineering in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of neuromorphic engineering' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of neuromorphic engineering.

Author: Andreas Munding
Publisher: Cuvillier Verlag
ISBN: 3867274061
Category :
Languages : en
Pages : 137

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Author: Poramate Manoonpong
Publisher: Frontiers Media SA
ISBN: 2889456056
Category :
Languages : en
Pages : 278

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How can neural and morphological computations be effectively combined and realized in embodied closed-loop systems (e.g., robots) such that they can become more like living creatures in their level of performance? Understanding this will lead to new technologies and a variety of applications. To tackle this research question, here, we bring together experts from different fields (including Biology, Computational Neuroscience, Robotics, and Artificial Intelligence) to share their recent findings and ideas and to update our research community. This eBook collects 17 cutting edge research articles, covering neural and morphological computations as well as the transfer of results to real world applications, like prosthesis and orthosis control and neuromorphic hardware implementation.

Author: James Giordano
Publisher: CRC Press
ISBN: 1439825866
Category : Science
Languages : en
Pages : 357

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New technologies that allow us to investigate mechanisms and functions of the brain have shown considerable promise in treating brain disease and injury. These emerging technologies also provide a means to assess and manipulate human consciousness, cognitions, emotions, and behaviors, bringing with them the potential to transform society. Neurotechnology: Premises, Potential, and Problems explores the technical, moral, legal, and sociopolitical issues that arise in and from today’s applications of neuroscience and technology and discusses their implications for the future. Some of the issues raised in this thought-provoking volume include: Neurotechnology in education: an enablement, a treatment, or an enhancement? The potential and limitations of neuroimaging technology in determining patient prognoses Tissue implantation technology as a way of engendering personalized medicine Neuroprostheses: restoration of functions of the disabled vs. enhancement to transhuman capabilities Deep brain stimulation and its use in restoring, preserving, or changing patients’ personal identity The benefit and risk of cognitive performance tools Cyborg technology and its potential to change our vision of humanity Methodologies for reducing the risk of neurotechnology’s impact on ethical, legal, and social issues With contributions from an international group of experts working on the cutting edge of neurotechnology, this volume lays the groundwork to appreciate the ethical, legal, and social aspects of the science in ways that keep pace with this rapidly progressing field.

Author: Robert Kozma
Publisher: Academic Press
ISBN: 0323958168
Category : Computers
Languages : en
Pages : 398

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Book Description
Artificial Intelligence in the Age of Neural Networks and Brain Computing, Second Edition demonstrates that present disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity, and smart autonomous search engines. The book covers the major basic ideas of "brain-like computing" behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as possible future alternatives. The present success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel, and Amazon, can be interpreted using the perspective presented in this book by viewing the co-existence of a successful synergism among what is referred to as computational intelligence, natural intelligence, brain computing, and neural engineering. The new edition has been updated to include major new advances in the field, including many new chapters. Developed from the 30th anniversary of the International Neural Network Society (INNS) and the 2017 International Joint Conference on Neural Networks (IJCNN Authored by top experts, global field pioneers, and researchers working on cutting-edge applications in signal processing, speech recognition, games, adaptive control and decision-making Edited by high-level academics and researchers in intelligent systems and neural networks Includes all new chapters, including topics such as Frontiers in Recurrent Neural Network Research; Big Science, Team Science, Open Science for Neuroscience; A Model-Based Approach for Bridging Scales of Cortical Activity; A Cognitive Architecture for Object Recognition in Video; How Brain Architecture Leads to Abstract Thought; Deep Learning-Based Speech Separation and Advances in AI, Neural Networks

Author: Mark A. Bedau
Publisher: MIT Press
ISBN: 9780262522908
Category : Computers
Languages : en
Pages : 584

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Book Description
The term "artificial life" describes research into synthetic systems that possess some of the essential properties of life. This interdisciplinary field includes biologists, computer scientists, physicists, chemists, geneticists, and others. Artificial life may be viewed as an attempt to understand high-level behavior from low-level rules—for example, how the simple interactions between ants and their environment lead to complex trail-following behavior. An understanding of such relationships in particular systems can suggest novel solutions to complex real-world problems such as disease prevention, stock-market prediction, and data mining on the Internet. Since their inception in 1987, the Artificial Life meetings have grown from small workshops to truly international conferences, reflecting the field's increasing appeal to researchers in all areas of science.

Author: Phillip Rowcliffe
Publisher:
ISBN:
Category :
Languages : en
Pages : 314

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Author: Mikhail Lebedev
Publisher: Frontiers Media SA
ISBN: 2889456145
Category : Neurosciences. Biological psychiatry. Neuropsychiatry
Languages : en
Pages : 666

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Book Description
Volume I, entitled “Augmentation of Brain Functions: Brain-Machine Interfaces”, is a collection of articles on neuroprosthetic technologies that utilize brain-machine interfaces (BMIs). BMIs strive to augment the brain by linking neural activity, recorded invasively or noninvasively, to external devices, such as arm prostheses, exoskeletons that enable bipedal walking, means of communication and technologies that augment attention. In addition to many practical applications, BMIs provide useful research tools for basic science. Several articles cover challenges and controversies in this rapidly developing field, such as ways to improve information transfer rate. BMIs can be applied to the awake state of the brain and to the sleep state, as well. BMIs can augment action planning and decision making. Importantly, BMI operations evoke brain plasticity, which can have long-lasting effects. Advanced neural decoding algorithms that utilize optimal feedback controllers are key to the BMI performance. BMI approach can be combined with the other augmentation methods; such systems are called hybrid BMIs. Overall, it appears that BMI will lead to many powerful and practical brain-augmenting technologies in the future.