The text discusses the techniques of deep learning and machine learning in the field of neuroscience, engineering approaches to study the brain structure and dynamics, convolutional networks for fast, energy-efficient neuromorphic computing, and reinforcement learning in feedback control. It showcases case studies in neural data analysis. The book- •Focuses on neuron modeling, development, and direction of neural circuits to explain perception, behavior, and biologically inspired intelligent agents for decision making. •Showcases important aspects such as human behavior prediction using smart technologies and understanding the modeling of nervous systems. •Discusses nature-inspired algorithms such as swarm intelligence, ant colony optimization, and multi-agent systems. •Presents information-theoretic, control-theoretic, and decision-theoretic approaches in neuroscience. •Includes case studies in functional magnetic resonance imaging (fMRI) and neural data analysis. This reference text addresses different applications of computational neurosciences using artificial intelligence, deep learning, and other machine learning techniques to fine-tune the models thereby solving the real-life problems prominently. It will further discuss important topics such as neural rehabilitation, brain-computer interfacing, neural control, neural system analysis, and neurobiologically inspired self-monitoring systems. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, information technology, and biomedical engineering.

The Performance of a system depends directly on the time required to perform an operation and number of these operations that can be performed concurrently. High performance computing systems can be designed using parallel processing. The effectiveness of these parallel systems rests primarily on the communication network linking processors and memory modules. Hence, an interconnection network that provides the desired connectivity and performance at minimum cost is required for communication in parallel processing systems. Multistage interconnection networks provide a compromise between shared bus and crossbar networks.