Neural Plasticity For Rich And Uncertain Robotic Information Streams
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| Author | : Andrea Soltoggio |
| Publisher | : Frontiers Media SA |
| Total Pages | : 85 |
| Release | : 2016-10-31 |
| Genre | : Neurosciences. Biological psychiatry. Neuropsychiatry |
| ISBN | : 2889199959 |
Models of adaptation and neural plasticity are often demonstrated in robotic scenarios with heavily pre-processed and regulated information streams to provide learning algorithms with appropriate, well timed, and meaningful data to match the assumptions of learning rules. On the contrary, natural scenarios are often rich of raw, asynchronous, overlapping and uncertain inputs and outputs whose relationships and meaning are progressively acquired, disambiguated, and used for further learning. Therefore, recent research efforts focus on neural embodied systems that rely less on well timed and pre-processed inputs, but rather extract autonomously relationships and features in time and space. In particular, realistic and more complete models of plasticity must account for delayed rewards, noisy and ambiguous data, emerging and novel input features during online learning. Such approaches model the progressive acquisition of knowledge into neural systems through experience in environments that may be affected by ambiguities, uncertain signals, delays, or novel features.
| Author | : Andy Clark |
| Publisher | : Oxford University Press, USA |
| Total Pages | : 425 |
| Release | : 2016 |
| Genre | : Medical |
| ISBN | : 0190217014 |
Exciting new theories in neuroscience, psychology, and artificial intelligence are revealing minds like ours as predictive minds, forever trying to guess the incoming streams of sensory stimulation before they arrive. In this up-to-the-minute treatment, philosopher and cognitive scientist Andy Clark explores new ways of thinking about perception, action, and the embodied mind.
| Author | : |
| Publisher | : |
| Total Pages | : 1134 |
| Release | : 1989 |
| Genre | : Aeronautics |
| ISBN | : |
| Author | : G. Buzsáki |
| Publisher | : Oxford University Press |
| Total Pages | : 465 |
| Release | : 2011 |
| Genre | : Medical |
| ISBN | : 0199828237 |
Studies of mechanisms in the brain that allow complicated things to happen in a coordinated fashion have produced some of the most spectacular discoveries in neuroscience. This book provides eloquent support for the idea that spontaneous neuron activity, far from being mere noise, is actually the source of our cognitive abilities. It takes a fresh look at the coevolution of structure and function in the mammalian brain, illustrating how self-emerged oscillatory timing is the brain's fundamental organizer of neuronal information. The small-world-like connectivity of the cerebral cortex allows for global computation on multiple spatial and temporal scales. The perpetual interactions among the multiple network oscillators keep cortical systems in a highly sensitive "metastable" state and provide energy-efficient synchronizing mechanisms via weak links. In a sequence of "cycles," György Buzsáki guides the reader from the physics of oscillations through neuronal assembly organization to complex cognitive processing and memory storage. His clear, fluid writing-accessible to any reader with some scientific knowledge-is supplemented by extensive footnotes and references that make it just as gratifying and instructive a read for the specialist. The coherent view of a single author who has been at the forefront of research in this exciting field, this volume is essential reading for anyone interested in our rapidly evolving understanding of the brain.
| Author | : David Eagleman |
| Publisher | : Vintage |
| Total Pages | : 258 |
| Release | : 2015-10-06 |
| Genre | : Science |
| ISBN | : 1101870540 |
From the renowned neuroscientist and New York Times bestselling author of Incognito comes the companion volume to the international PBS series about how your life shapes your brain, and how your brain shapes your life. "An ideal introduction to how biology generates the mind.... Clear, engaging and thought-provoking." —Nature Locked in the silence and darkness of your skull, your brain fashions the rich narratives of your reality and your identity. Join renowned neuroscientist David Eagleman for a journey into the questions at the mysterious heart of our existence. What is reality? Who are “you”? How do you make decisions? Why does your brain need other people? How is technology poised to change what it means to be human? In the course of his investigations, Eagleman guides us through the world of extreme sports, criminal justice, facial expressions, genocide, brain surgery, gut feelings, robotics, and the search for immortality. Strap in for a whistle-stop tour into the inner cosmos. In the infinitely dense tangle of billions of brain cells and their trillions of connections, something emerges that you might not have expected to see in there: you. Color illustrations throughout.
| Author | : Qiang Yang |
| Publisher | : Springer Nature |
| Total Pages | : 291 |
| Release | : 2020-11-25 |
| Genre | : Computers |
| ISBN | : 3030630765 |
This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”
| Author | : Kevin J. Mitchell |
| Publisher | : Princeton University Press |
| Total Pages | : 305 |
| Release | : 2020-03-31 |
| Genre | : Psychology |
| ISBN | : 0691204152 |
"What makes you the way you are--and what makes each of us different from everyone else? In Innate, leading neuroscientist and popular science blogger Kevin Mitchell traces human diversity and individual differences to their deepest level: in the wiring of our brains. Deftly guiding us through important new research, including his own groundbreaking work, he explains how variations in the way our brains develop before birth strongly influence our psychology and behavior throughout our lives, shaping our personality, intelligence, sexuality, and even the way we perceive the world. We all share a genetic program for making a human brain, and the program for making a brain like yours is specifically encoded in your DNA. But, as Mitchell explains, the way that program plays out is affected by random processes of development that manifest uniquely in each person, even identical twins. The key insight of Innate is that the combination of these developmental and genetic variations creates innate differences in how our brains are wired--differences that impact all aspects of our psychology--and this insight promises to transform the way we see the interplay of nature and nurture. Innate also explores the genetic and neural underpinnings of disorders such as autism, schizophrenia, and epilepsy, and how our understanding of these conditions is being revolutionized. In addition, the book examines the social and ethical implications of these ideas and of new technologies that may soon offer the means to predict or manipulate human traits. Compelling and original, Innate will change the way you think about why and how we are who we are."--Provided by the publisher.
| Author | : Jeffrey L Krichmar |
| Publisher | : Frontiers Media SA |
| Total Pages | : 159 |
| Release | : 2015-03-05 |
| Genre | : Neurosciences. Biological psychiatry. Neuropsychiatry |
| ISBN | : 2889194310 |
Organisms are equipped with value systems that signal the salience of environmental cues to their nervous system, causing a change in the nervous system that results in modification of their behavior. These systems are necessary for an organism to adapt its behavior when an important environmental event occurs. A value system constitutes a basic assumption of what is good and bad for an agent. These value systems have been effectively used in robotic systems to shape behavior. For example, many robots have used models of the dopaminergic system to reinforce behavior that leads to rewards. Other modulatory systems that shape behavior are acetylcholine’s effect on attention, norepinephrine’s effect on vigilance, and serotonin’s effect on impulsiveness, mood, and risk. Moreover, hormonal systems such as oxytocin and its effect on trust constitute as a value system. This book presents current research involving neurobiologically inspired robots whose behavior is: 1) Shaped by value and reward learning, 2) adapted through interaction with the environment, and 3) shaped by extracting value from the environment.
| Author | : William L. William L. Hamilton |
| Publisher | : Springer Nature |
| Total Pages | : 141 |
| Release | : 2022-06-01 |
| Genre | : Computers |
| ISBN | : 3031015886 |
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
| Author | : Yunhui Liu |
| Publisher | : CRC Press |
| Total Pages | : 343 |
| Release | : 2011-12-21 |
| Genre | : Medical |
| ISBN | : 1439854882 |
Robotic engineering inspired by biology—biomimetics—has many potential applications: robot snakes can be used for rescue operations in disasters, snake-like endoscopes can be used in medical diagnosis, and artificial muscles can replace damaged muscles to recover the motor functions of human limbs. Conversely, the application of robotics technology to our understanding of biological systems and behaviors—biorobotic modeling and analysis—provides unique research opportunities: robotic manipulation technology with optical tweezers can be used to study the cell mechanics of human red blood cells, a surface electromyography sensing system can help us identify the relation between muscle forces and hand movements, and mathematical models of brain circuitry may help us understand how the cerebellum achieves movement control. Biologically Inspired Robotics contains cutting-edge material—considerably expanded and with additional analysis—from the 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO). These 16 chapters cover both biomimetics and biorobotic modeling/analysis, taking readers through an exploration of biologically inspired robot design and control, micro/nano bio-robotic systems, biological measurement and actuation, and applications of robotics technology to biological problems. Contributors examine a wide range of topics, including: A method for controlling the motion of a robotic snake The design of a bionic fitness cycle inspired by the jaguar The use of autonomous robotic fish to detect pollution A noninvasive brain-activity scanning method using a hybrid sensor A rehabilitation system for recovering motor function in human hands after injury Human-like robotic eye and head movements in human–machine interactions A state-of-the-art resource for graduate students and researchers in the fields of control engineering, robotics, and biomedical engineering, this text helps readers understand the technology and principles in this emerging field.
