Neuromorphic Engineering: Brain-Inspired Computing and Systems explores the transformative field of neuromorphic engineering, which mimics the human brain's structure and function to enhance computing. The monograph covers the field's origins, highlighting its advantages over conventional computing in parallel processing, power efficiency, and adaptability. It delves into biological inspirations, including neural networks and synaptic plasticity, and compares neuromorphic hardware to traditional systems. The text discusses brain-inspired algorithms such as Spiking Neural Networks (SNNs), Hebbian learning, and Spike-Timing-Dependent Plasticity (STDP). Applications in robotics, autonomous systems, edge computing, IoT, brain-machine interfaces (BMIs), and AI are examined. It also addresses challenges like scalability, power efficiency, and ethical issues, and considers future research directions, including integration with quantum computing. This resource is essential for understanding neuromorphic engineering’s potential impact on future computing technologies.

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