Biomimetic Neural Learning For Intelligent Robots

This state-of-the-art survey contains selected papers contributed by researchers in intelligent systems, cognitive robotics, and neuroscience including contributions from the MirrorBot project and from the NeuroBotics Workshop 2004. The research work presented demonstrates significant novel developments in biologically inspired neural models for use in intelligent robot environments and biomimetic cognitive behavior.

The multidisciplinary issues involved in the development of biologically inspired intelligent robots include materials, actuators, sensors, structures, functionality, control, intelligence, and autonomy. This book reviews various aspects ranging from the biological model to the vision for the future.

Biomimetic research is an emerging field that aims to draw inspiration and substances from natural sources and create biological systems in structure, mechanism, and function through robotics. The products have a wide array of application including surgical robots, prosthetics, neurosurgery, and biomedical image analysis. The Handbook of Research on Biomimetics and Biomedical Robotics provides emerging research on robotics, mechatronics, and the application of biomimetic design. While highlighting mechatronical challenges in today’s society, readers will find new opportunities and innovations in design capabilities in intelligent robotics and interdisciplinary biomedical products. This publication is a vital resource for senior and graduate students, researchers, and scientists in engineering seeking current research on best ways to globally expand online higher education.

Nature has always been an inspiration to humans in terms of using minimum resources to produce maximum results, and in its ability to allow organisms to operate and fit the required environment. There are a number of challenges for humans attempting to mimic nature in this regard given the endless possibilities, such as in using techno-biomimetic devices, fully-grown intelligent robots, autonomous systems and vehicles, molecular computers and nanotechnological materials, which are currently being are developed. This book investigates the various advantages, challenges and limitations of data science and artificial intelligence in techno-biomimetic systems.

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic.

An introduction to how neuroethology can inform the development of robots controlled by synaptic networks instead of algorithms, from a pioneer in biorobotics. The trait most fundamental to the evolution of animals is the capability to adapt to novel circumstances in unpredictable environments. Recent advances in biomimetics have made it feasible to construct robots modeled on such unsupervised autonomous behavior, and animal models provide a library of existence proofs. Filling an important gap in the field, this introductory textbook illuminates how neurobiological principles can inform the development of robots that are controlled by synaptic networks, as opposed to algorithms. Joseph Ayers provides a comprehensive overview of the sensory and motor systems of a variety of model biological systems and shows how their behaviors may be implemented in artificial systems, such as biomimetic robots. Introduces the concept of biological intelligence as applied to robots, building a strategy for autonomy based on the neuroethology of simple animal models Provides a mechanistic physiological framework for the control of innate behavior Illustrates how biomimetic vehicles can be operated in the field persistently and adaptively Developed by a pioneer in biorobotics with decades of teaching experience Proven in the classroom Suitable for professionals and researchers as well as undergraduate and graduate students in cognitive science and computer science

The adult brain is not as hard-wired as traditionally thought. By modifying their small- or large-scale morphology, neurons can make new synaptic connections or break existing ones (structural plasticity). Structural changes accompany memory formation and learning, and are induced by neurogenesis, neurodegeneration and brain injury such as stroke. Exploring the role of structural plasticity in the brain can be greatly assisted by mathematical and computational models, as they enable us to bridge the gap between system-level dynamics and lower level cellular and molecular processes. However, most traditional neural network models have fixed neuronal morphologies and a static connectivity pattern, with plasticity merely arising from changes in the strength of existing synapses (synaptic plasticity). In The Rewiring Brain, the editors bring together for the first time contemporary modeling studies that investigate the implications of structural plasticity for brain function and pathology. Starting with an experimental background on structural plasticity in the adult brain, the book covers computational studies on homeostatic structural plasticity, the impact of structural plasticity on cognition and cortical connectivity, the interaction between synaptic and structural plasticity, neurogenesis-related structural plasticity, and structural plasticity in neurological disorders. Structural plasticity adds a whole new dimension to brain plasticity, and The Rewiring Brain shows how computational approaches may help to gain a better understanding of the full adaptive potential of the adult brain. The book is written for both computational and experimental neuroscientists. Reviews the current state of knowledge of structural plasticity in the adult brain Gives a comprehensive overview of computational studies on structural plasticity Provides insights into the potential driving forces of structural plasticity and the functional implications of structural plasticity for learning and memory Serves as inspiration for developing novel treatment strategies for stimulating functional repair after brain damage