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Python in Neuroscience
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.
Handbook of Brain Microcircuits
In order to focus on principles, each chapter in this work is brief, organized around 1-3 wiring diagrams of the key circuits, with several pages of text that distil the functional significance of each microcircuit
Homeostatic and Retrograde Signaling Mechanisms Modulating Presynaptic Function and Plasticity
Activity within neural circuits shapes the synaptic properties of component neurons in a manner that maintains stable excitatory drive, a process referred to as homeostatic plasticity. These potent and adaptive mechanisms have been demonstrated to modulate activity at the level of an individual neuron, synapse, circuit, or entire network, and dysregulation at some or all of these levels may contribute to neuropsychiatric disorders, intellectual disability, and epilepsy. Greater mechanistic understanding of homeostatic plasticity will provide key insights into the etiology of these disorders, which may result from network instability and synaptic dysfunction. Over the past 15 years, the molec...
The Age of Em
Robots may one day rule the world, but what is a robot-ruled Earth like? Many think the first truly smart robots will be brain emulations or ems. Scan a human brain, then run a model with the same connections on a fast computer, and you have a robot brain, but recognizably human. Train an em to do some job and copy it a million times: an army of workers is at your disposal. When they can be made cheaply, within perhaps a century, ems will displace humans in most jobs. In this new economic era, the world economy may double in size every few weeks. Some say we can't know the future, especially following such a disruptive new technology, but Professor Robin Hanson sets out to prove them wrong. ...
The Cortex and the Critical Point
How the cerebral cortex operates near a critical phase transition point for optimum performance. Individual neurons have limited computational powers, but when they work together, it is almost like magic. Firing synchronously and then breaking off to improvise by themselves, they can be paradoxically both independent and interdependent. This happens near the critical point: when neurons are poised between a phase where activity is damped and a phase where it is amplified, where information processing is optimized, and complex emergent activity patterns arise. The claim that neurons in the cortex work best when they operate near the critical point is known as the criticality hypothesis. In th...
Neuromorphic Engineering
The brain is not a glorified digital computer. It does not store information in registers, and it does not mathematically transform mental representations to establish perception or behavior. The brain cannot be downloaded to a computer to provide immortality, nor can it destroy the world by having its emerged consciousness traveling in cyberspace. However, studying the brain's core computation architecture can inspire scientists, computer architects, and algorithm designers to think fundamentally differently about their craft. Neuromorphic engineers have the ultimate goal of realizing machines with some aspects of cognitive intelligence. They aspire to design computing architectures that co...
Worlding the Brain
Moving beyond the neurohype of recent decades, this book introduces the concept of worlding as a new way to understand the inherent entanglement of brains/minds with their worldly environments, cultural practices, and social contexts. Case studies ranging from film, literature, music, and dance to pedagogy, historical trauma, and present-day discourses of mindfulness investigate how brains are worlded in an active interplay of biological, cognitive, and socio-discursive factors. Combining scholarly work with personal accounts of neurodiversity and essays by artists reflecting on their practical engagement with cognition, Worlding the Brain makes a case for the distinctive role of the humanities and arts in the study of brains and cognition and explores novel forms interdisciplinarity.
Methods for Synaptic Interrogation
This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
Cameron's Thesis
Both the visualisation and management of large-scale computer hardware is difficult due to its distributed nature. This thesis develops a framework to support both these goals on the SpiNNaker neural network architecture - which can scale to more than a million processors. The solution provides visualisation and management to the SpiNNaker machine, traversing the hardware and software divide to provide a unified solution for the real-time monitoring of artificial neural networks, and the SpiNNaker hardware on which it runs. This book is the story of its development
Energy Efficiency and Robustness of Advanced Machine Learning Architectures
Machine Learning (ML) algorithms have shown a high level of accuracy, and applications are widely used in many systems and platforms. However, developing efficient ML-based systems requires addressing three problems: energy-efficiency, robustness, and techniques that typically focus on optimizing for a single objective/have a limited set of goals. This book tackles these challenges by exploiting the unique features of advanced ML models and investigates cross-layer concepts and techniques to engage both hardware and software-level methods to build robust and energy-efficient architectures for these advanced ML networks. More specifically, this book improves the energy efficiency of complex m...
