Angielski
Primer to Neuromorphic Computing highlights critical and ongoing research into the diverse applications of neuromorphic computing. It includes an overview of primary scientific concepts for the research topic of neuromorphic computing, such as neurons as computational units, artificial intelligence, machine learning and neuromorphic models. It also discusses the fundamental design method and organization of neuromorphic architecture. Hardware for neuromorphic systems can be developed by exploiting the magnetic properties of different materials. These systems are more energy efficient and enable faster computation . Magnetic tunnel junctions and magnetic textures can be employed to act as neurons and synapses. Neuromorphic systems have general intelligence like humans as they can apply knowledge gained in one domain to other domains. Discusses potential neuromorphic applications in computingPresents current trends and models in neuromorphic computing and neural network hardware architecturesShows the development of novel devices and hardware to enable neuromorphic computingOffers information about computation and learning principles for neuromorphic systemsPresents information about Neuromorphic implementations of neurobiological learning algorithmsDiscusses biologically inspired neuromorphic systems and devices (including adaptive bio interfacing and hybrid systems consisting of living matter and synthetic matter)Presents a systematic way to design applications algorithms and hardware to achieve the best performance
