Understanding and implementing the brain's computational paradigm is the one true grand challenge facing computer researchers. Not only are the brain's computational capabilities far beyond those of conventional computers, its energy efficiency is truly remarkable. This book, written from the perspective of a computer designer and targeted at computer researchers, is intended to give both background and lay out a course of action for studying the brain's computational paradigm. It contains a mix of concepts and ideas drawn from computational neuroscience, combined with those of the author. As background, relevant biological features are described in terms of their computational and communication properties. The brain's neocortex is constructed of massively interconnected neurons that compute and communicate via voltage spikes, and a strong argument can be made that precise spike timing is an essential element of the paradigm. Drawing from the biological features, a mathematics-based computational paradigm is constructed. The key feature is spiking neurons that perform communication and processing in space-time, with emphasis on time. In these paradigms, time is used as a freely available resource for both communication and computation. Neuron models are first discussed in general, and one is chosen for detailed development. Using the model, single-neuron computation is first explored. Neuron inputs are encoded as spike patterns, and the neuron is trained to identify input pattern similarities. Individual neurons are building blocks for constructing larger ensembles, referred to as "columns". These columns are trained in an unsupervised manner and operate collectively to perform the basic cognitive function of pattern clustering. Similar input patterns are mapped to a much smaller set of similar output patterns, thereby dividing the input patterns into identifiable clusters. Larger cognitive systems are formed by combining columns into a hierarchical architecture. These higher level architectures are the subject of ongoing study, and progress to date is described in detail in later chapters. Simulation plays a major role in model development, and the simulation infrastructure developed by the author is described.

The human brain, which is considered to be the prototypal biological computer, in its current incarnation is the result of more than a billion years of evolution. Its main functions have always been to regulate the internal milieu and to help the organism/being to survive and reproduce. With growing complexity, the brain has adopted a number of design principles that serve to maximize its efficiency in performing a broad range of tasks. The physical computer, on the other hand, has had only 200 years or so to evolve, and its perceived purpose is considerably different and far more constrained - that is, to solve a set of mathematical functions. This picture is rapidly changing however. One may argue that the functions of brains and computers are converging. This transition comes at a critical time when the roadmap for physical computing is becoming murky after a long period of exponential growth. Hence the existential questions arise if the underlying design principles may converge or cross-breed, or if the different mechanisms (physics versus biology) will always translate into radically different solutions. Neuromorphic systems are just one possible form of cross-fertilization between biological and physical computing. Other neural concepts at different levels of abstraction can help inspire us to rethink how to efficiently perform a number of meaningful tasks and functions. This leads to the topic of this monograph. This monograph reviews some of the insights arising from both computational neuroscience and computer engineering, and evaluates how these could combine to help us build a next generation of "computing" systems. To create insights and identify opportunities, this monograph firstly puts neural and physical computing face-to-face, and compares how they arose, how they differ right now with respect to a number of metrics such as computational and power density, and how these metrics may change over the future decades. A similar analysis is performed at the architectural/computational model level. While doing so, ground truths in terms of obtainable performance, bandwidth and power/energy efficiency are established. Thereafter, a number of neural design principles that may translate into design guidelines for future computers are identified. On close examination of these and other observations, it becomes apparent that cross-fertilization between the domains is already happening at multiple levels, albeit in an incremental way. The publication completes with perspectives on where brain-inspired computing may lead us, some speculative bets, and a number of forward-looking reflections.

Python is rapidly becoming the de facto standard language for systems integration. Python has a large user and developer-base external to theneuroscience community, and a vast module library that facilitates rapid and maintainable development of complex and intricate systems. In this Research Topic, we highlight recent efforts to develop Python modules for the domain of neuroscience software and neuroinformatics: - simulators and simulator interfaces - data collection and analysis - sharing, re-use, storage and databasing of models and data - stimulus generation - parameter search and optimization - visualization - VLSI hardware interfacing. Moreover, we seek to provide a representative overview of existing mature Python modules for neuroscience and neuroinformatics, to demonstrate a critical mass and show that Python is an appropriate choice of interpreter interface for future neuroscience software development.

This open access book constitutes revised selected papers from the 4th International Workshop on Brain-Inspired Computing, BrainComp 2019, held in Cetraro, Italy, in July 2019. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with research on brain atlasing, multi-scale models and simulation, HPC and data infra-structures for neuroscience as well as artificial and natural neural architectures.

Neuromorphic electronic engineering takes its inspiration from the functioning of nervous systems to build more power efficient electronic sensors and processors. Event-based neuromorphic systems are inspired by the brain's efficient data-driven communication design, which is key to its quick responses and remarkable capabilities. This cross-disciplinary text establishes how circuit building blocks are combined in architectures to construct complete systems. These include vision and auditory sensors as well as neuronal processing and learning circuits that implement models of nervous systems. Techniques for building multi-chip scalable systems are considered throughout the book, including methods for dealing with transistor mismatch, extensive discussions of communication and interfacing, and making systems that operate in the real world. The book also provides historical context that helps relate the architectures and circuits to each other and that guides readers to the extensive literature. Chapters are written by founding experts and have been extensively edited for overall coherence. This pioneering text is an indispensable resource for practicing neuromorphic electronic engineers, advanced electrical engineering and computer science students and researchers interested in neuromorphic systems. Key features: Summarises the latest design approaches, applications, and future challenges in the field of neuromorphic engineering. Presents examples of practical applications of neuromorphic design principles. Covers address-event communication, retinas, cochleas, locomotion, learning theory, neurons, synapses, floating gate circuits, hardware and software infrastructure, algorithms, and future challenges.