Evolvable Hardware (EHW) has emerged as a sub-domain of artificial evolution represented by a design methodology (consortium of methods) involving the application of Evolutionary Algorithms (EA) to the synthesis of digital and analogue electronic circuits and systems. Nevertheless, the most benefit for the society and indeed most revolutionizing application of EA is its hardware implementation leading to the EHW. These new EA based methodologies led to a new type of machines that is evolved to attain a desired behaviour, which means they have a behavioural computational intelligence. EHW is a special case of the adaptive hardware, namely being strongly related to the Adaptive Systems (AS) and the Adaptive Hardware (AH). The book presents a careful selection of the field that very well reflects the breadth of this high technology and its terminology and applications in context of the AS/AH. The harmonious symbiosis of the engineering approach and the accurate scientific methodology features the aspects of highly relevant and practical design principles governing the development of EHW and its connections with AS/AH. This book is both attractive and useful for everybody interested in the design and analysis of EHW in context of AS/AH and implementation of real time adaptive hardware hybrid intelligent systems.

Evolvable Hardware (EHW) has emerged as a sub-domain of artificial evolution represented by a design methodology (consortium of methods) involving the application of Evolutionary Algorithms (EA) to the synthesis of digital and analogue electronic circuits and systems. Nevertheless, the most benefit for the society and indeed most revolutionizing application of EA is its hardware implementation leading to the EHW. These new EA based methodologies led to a new type of machines that is evolved to attain a desired behaviour, which means they have a behavioural computational intelligence. EHW is a special case of the adaptive hardware, namely being strongly related to the Adaptive Systems (AS) and the Adaptive Hardware (AH). The book presents a careful selection of the field that very well reflects the breadth of this high technology and its terminology and applications in context of the AS/AH. The harmonious symbiosis of the engineering approach and the accurate scientific methodology features the aspects of highly relevant and practical design principles governing the development of EHW and its connections with AS/AH. This book is both attractive and useful for everybody interested in the design and analysis of EHW in context of AS/AH and implementation of real time adaptive hardware hybrid intelligent systems.

Neuroscientists are increasingly becoming more interested in modelling brain functions where capturing the biophysical mechanisms underpinning these functions requires plausible models at the level of neuron cells. However, cell level models are still very much in the embryo stage and therefore there is a need to advance the level of biological realism at the level of neurons/synapses. Recent publications have highlighted that astrocytes continually exchange information with multiple synapses; if we are to fully appreciate this dynamic and coordinated interplay between these cells then more research on bidirectional signalling between astrocytes and neurons is required. A better understanding of astrocyte-neuron cell coupling would provide the building block for studying the regulatory capability of astrocytes networks on a large scale. For example, it is believed that local and global signalling via astrocytes underpins brain functions like synchrony, learning, memory and self repair. This Research Topic aims to report on current research work which focuses on understanding and modelling the interaction between astrocytes and neurons at the cellular level (Bottom up) and at network level (Top down). Understanding astrocytic regulation of neural activity is crucial if we are to capture how information is represented and processed across large neuronal ensembles in humans.

A comprehensive introduction to new approaches in artificial intelligence and robotics that are inspired by self-organizing biological processes and structures. New approaches to artificial intelligence spring from the idea that intelligence emerges as much from cells, bodies, and societies as it does from evolution, development, and learning. Traditionally, artificial intelligence has been concerned with reproducing the abilities of human brains; newer approaches take inspiration from a wider range of biological structures that that are capable of autonomous self-organization. Examples of these new approaches include evolutionary computation and evolutionary electronics, artificial neural networks, immune systems, biorobotics, and swarm intelligence—to mention only a few. This book offers a comprehensive introduction to the emerging field of biologically inspired artificial intelligence that can be used as an upper-level text or as a reference for researchers. Each chapter presents computational approaches inspired by a different biological system; each begins with background information about the biological system and then proceeds to develop computational models that make use of biological concepts. The chapters cover evolutionary computation and electronics; cellular systems; neural systems, including neuromorphic engineering; developmental systems; immune systems; behavioral systems—including several approaches to robotics, including behavior-based, bio-mimetic, epigenetic, and evolutionary robots; and collective systems, including swarm robotics as well as cooperative and competitive co-evolving systems. Chapters end with a concluding overview and suggested reading.

Robotic exoskeletons that allow stroke survivors to regain use of their limbs, 3D-printed replacement body parts, and dozens of other innovations still in schematic design are revolutionizing the treatment of debilitating injuries and nervous system disorders. What all these technologies have in common is that they are modeled after engineering strategies found in nature—strategies developed by a vast array of organisms over eons of evolutionary trial and error. Eugene Goldfield lays out many principles of engineering found in the natural world, with a focus on how evolutionary and developmental adaptations, such as sensory organs and spinal cords, function within complex organisms. He shows how the component parts of highly coordinated structures organize themselves into autonomous functional systems. For example, when people walk, spinal cord neurons generate coordinated signals that continuously reorganize patterns of muscle activations during the gait cycle. This self-organizing capacity is just one of many qualities that allow biological systems to be robust, adaptive, anticipatory, and self-repairing. To exploit the full potential of technologies designed to interact seamlessly with human bodies, properties like these must be better understood and harnessed at every level, from molecules to cells to organ systems. Bioinspired Devices brings together insights from a wide range of fields. A member of the Wyss Institute for Biologically Inspired Engineering, Goldfield offers an insider’s view of cutting-edge research, and envisions a future in which synthetic and biological devices share energy sources and control, blurring the boundary between nature and medicine.

"The objective of the book is to introduce and bring together well-known circuit design aspects, as well as to cover up-to-date outcomes of theoretical studies in decision-making, biologically-inspired, and artificial intelligent learning techniques"--Provided by publisher.