In today's world, where the demand for advanced computing systems has skyrocketed, energy efficiency has become a top priority. The development of energy-efficient neuromorphic computing systems has gained significant attention due to their ability to mimic the human brain's low-power, high-performance computing capabilities. The field of neuromorphic computing is at the forefront of research and development in emerging technologies such as artificial intelligence, robotics, and cognitive computing. Energy-Efficient Devices and Circuits for Neuromorphic Computing is an important contribution to the field of neuromorphic computing. The book covers a wide range of topics, from the fundamentals of neuron dynamics to the latest developments in energy-efficient CMOS devices and circuits, emerging post-CMOS devices, and non-volatile memory crossbar arrays for energy-efficient neuromorphic computing. It discusses the theoretical analysis of the learning process in spiking neural networks, two-terminal neuromorphic devices, material-engineered neuromorphic devices, and novel biomimetic Si devices for energy-efficient neuromorphic computing architecture. Overall, it will be an essential resource for researchers, engineers, and students working in the fields of neuromorphic computing and energy-efficient electronics.

• Comprehensive coverage of neuromorphic computing based upon energy-efficient electronic devices and circuits, providing a deep understanding of the principles and applications of these fields.
• Practical guidance and numerous examples, making it an excellent resource for researchers, engineers, and students designing energy-efficient neuromorphic computing systems.
• Detailed coverage of emerging post-CMOS devices such as memristors and MTJs and their potential applications in energy-efficient synapses and neurons, providing readers with a cutting-edge perspective on the latest developments in the field