In this lesson, you will learn about hardware for computing for non-ICT specialists.
This lecture covers different perspectives on the study of the mental, focusing on the difference between Mind and Brain.
This lecture covers a lot of post-war developments in the science of the mind, focusing first on the cognitive revolution, and concluding with living machines.
This talk describes the NIH-funded SPARC Data Structure, and how this project navigates ontology development while keeping in mind the FAIR science principles.
This talk enumerates the challenges regarding data accessibility and reusability inherent in the current scientific publication system, and discusses novel approaches to these challenges, such as the EBRAINS Live Papers platform.
This brief talk goes into work being done at The Alan Turing Institute to solve real-world challenges and democratize computer vision methods to support interdisciplinary and international researchers.
This lesson contains the first part of the lecture Data Science and Reproducibility. You will learn about the development of data science and what the term currently encompasses, as well as how neuroscience and data science intersect.
In this second part of the lecture Data Science and Reproducibility, you will learn how to apply the awareness of the intersection between neuroscience and data science (discussed in part one) to an understanding of the current reproducibility crisis in biomedical science and neuroscience.
This lesson aims to define computational neuroscience in general terms, while providing specific examples of highly successful computational neuroscience projects.
Computer arithmetic is necessarily performed using approximations to the real numbers they are intended to represent, and consequently it is possible for the discrepancies between the actual solution and the approximate solutions to diverge, i.e. to become increasingly different. This lecture focuses on how this happens and techniques for reducing the effects of these phenomena and discuss systems which are chaotic.
This lecture will addresses what it means for a problem to have a computable solution, methods for combining computability results to analyse more complicated problems, and finally look in detail at one particular problem which has no computable solution: the halting problem.
This lecture focuses on computational complexity, a concept which lies at the heart of computer science thinking. In short, it is a way to quickly gauge an approximation to the computational resource required to perform a task.
In this lesson, you will learn about the current challenges facing the integration of machine learning and neuroscience.
This lecture gives an introduction to simulation, models, and the neural simulation tool NEST.
This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.
This lecture covers structured data, databases, federating neuroscience-relevant databases, and ontologies.
This lesson discusses both state-of-the-art detection and prevention schema in working with neurodegenerative diseases.
This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.
This lesson discuses forms of neural plasticity on many levels, including short-term, long-term, metaplasticity, and structural plasticity. During the lesson you will also be presented with examples related to the modelling of biochemical networks.
This lesson provides an introduction to modelling of chemical computation in the brain.