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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.

memristor --- artificial synapse --- neuromorphic computing --- memristor-CMOS hybrid circuit --- temporal pooling --- sensory and hippocampal responses --- cortical neurons --- hierarchical temporal memory --- neocortex --- memristor-CMOS hybrid circuit --- defect-tolerant spatial pooling --- boost-factor adjustment --- memristor crossbar --- neuromorphic hardware --- memristor --- compact model --- emulator --- neuromorphic --- synapse --- STDP --- pavlov --- neuromorphic systems --- spiking neural networks --- memristors --- spike-timing-dependent plasticity --- RRAM --- vertical RRAM --- neuromorphics --- neural network hardware --- reinforcement learning --- AI --- neuromorphic computing --- multiscale modeling --- memristor --- optimization --- RRAM --- simulation --- memristors --- neuromorphic engineering --- OxRAM --- self-organization maps --- synaptic device --- memristor --- neuromorphic computing --- artificial intelligence --- hardware-based deep learning ICs --- circuit design --- memristor --- RRAM --- variability --- time series modeling --- autocovariance --- graphene oxide --- laser --- memristor --- crossbar array --- neuromorphic computing --- wire resistance --- synaptic weight --- character recognition --- neuromorphic computing --- Flash memories --- memristive devices --- resistive switching --- synaptic plasticity --- artificial neural network --- spiking neural network --- pattern recognition --- strongly correlated oxides --- resistive switching --- neuromorphic computing --- transistor-like devices --- artificial intelligence --- neural networks --- resistive switching --- memristive devices --- deep learning networks --- spiking neural networks --- electronic synapses --- crossbar array --- pattern recognition

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Flexible Electronics platforms are increasingly used in the fields of sensors, displays, and energy conversion with the ultimate goal of facilitating their ubiquitous integration in our daily lives. Some of the key advantages associated with flexible electronic platforms are: bendability, lightweight, elastic, conformally shaped, nonbreakable, roll-to-roll manufacturable, and large-area. To realize their full potential, however, it is necessary to develop new methods for the fabrication of multifunctional flexible electronics at a reduced cost and with an increased resistance to mechanical fatigue. Accordingly, this Special Issue seeks to showcase short communications, research papers, and review articles that focus on novel methodological development for the fabrication, and integration of flexible electronics in healthcare, environmental monitoring, displays and human-machine interactivity, robotics, communication and wireless networks, and energy conversion, management, and storage.

epidermal electronics --- wearable heater --- temperature sensor --- feedback control --- droplet circuits --- liquid metal --- quantum tunneling effect --- solution electronics --- electron transport --- ionic conduction --- quantum computing --- brain-like intelligence --- flexible organic electronics --- artificial synapses --- neuromorphic computing --- long-term plasticity --- flexible electronics --- nano-fabrication --- top-down approaches --- bottom-up approaches --- variable optical attenuator (VOA) --- surface plasmon-polariton (SPP) --- microwave photonics --- stretchability --- electronic measurements --- stretchable circuits --- design metrics --- reliability --- island-bridge --- conformal design --- non-developable surface --- stretchable electronics --- epidermal sensors --- stretchable electronics --- wireless power --- hydrophobic paper --- wearable stimulators --- paper electronics --- low-cost manufacture --- stretchable electronics --- tunnel encapsulation --- Polyvinyl Alcohol --- durability --- bio-integrated devices --- tissue adhesives --- tunable adhesion --- dry/wet conditions --- soft biological tissue --- n/a