Main description:

The book presents the field of neuromorphic engineering from the perspectives of the scientist, the algorithm designer, and the computer architect.
It covers the fundamentals of neuronal modeling, neuromorphic circuits, neuronal architectures, the neural engineering framework, and neuromorphic machine learning.
It zooms in and out of the different disciplines, allowing readers with diverse backgrounds to understand and appreciate the field.
The book walks the thin line of being descriptive enough to give the reader a clear understanding while not being too technical and tedious. It was designed for graduate students in the fields of computer science, electrical engineering, neuroscience, and computational biology.

Contents:

Part 1. Introduction and overview. 1. The Scientist Perspective. 2. Introducing the perspective of the computer architect. 3. Introducing the perspective of the algorithm designer. Part 2. The Scientist's Perspective. 4. Biological description of neuronal dynamic. 5. Models of point neuronal dynamic. 6. Models of morphologically detailed neurons. 7. Models of network dynamic and learning. Part 3. The Computer Architect's Perspective. 8. Neuromorphic hardware. 9. Communication and hybrid circuit design. 10. In-memory computing with memristors. Part 4. The Algorithms Designer's Perspective. 11. Introduction to neuromorphic programming. 12. The Neural Engineering Framework (NEF). 13. Learning spiking neural networks.