This lecture discusses the the importance and need for data sharing in clinical neuroscience.
This lecture gives insights into the Medical Informatics Platform's current and future data privacy model.
This lecture gives an overview on the European Health Dataspace.
This is the first of two workshops on reproducibility in science, during which participants are introduced to concepts of FAIR and open science. After discussing the definition of and need for FAIR science, participants are walked through tutorials on installing and using Github and Docker, the powerful, open-source tools for versioning and publishing code and software, respectively.
This lesson contains both a lecture and a tutorial component. The lecture (0:00-20:03 of YouTube video) discusses both the need for intersectional approaches in healthcare as well as the impact of neglecting intersectionality in patient populations. The lecture is followed by a practical tutorial in both Python and R on how to assess intersectional bias in datasets. Links to relevant code and data are found below.
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.
In this lecture, you will learn about current methods, approaches, and challenges to studying human neuroanatomy, particularly through the lense of neuroimaging data such as fMRI and diffusion tensor imaging (DTI).
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.
This lesson aims to define computational neuroscience in general terms, while providing specific examples of highly successful computational neuroscience projects.
This lecture describes how to build research workflows, including a demonstrate using DataJoint Elements to build data pipelines.
This lesson briefly goes over the outline of the Neuroscience for Machine Learners course.
In this lesson, you will learn about the current challenges facing the integration of machine learning and neuroscience.
This lesson delves into the the structure of one of the brain's most elemental computational units, the neuron, and how said structure influences computational neural network models.
In this lesson you will learn how machine learners and neuroscientists construct abstract computational models based on various neurophysiological signalling properties.
This lesson delves into the human nervous system and the immense cellular, connectomic, and functional sophistication therein.
This lesson describes spike timing-dependent plasticity (STDP), a biological process that adjusts the strength of connections between neurons in the brain, and how one can implement or mimic this process in a computational model. You will also find links for practical exercises at the bottom of this page.
In this lesson, you will learn about some of the many methods to train spiking neural networks (SNNs) with either no attempt to use gradients, or only use gradients in a limited or constrained way.
In this lesson, you will learn how to train spiking neural networks (SNNs) with a surrogate gradient method.
In this lesson, you will hear about some of the open issues in the field of neuroscience, as well as a discussion about whether neuroscience works, and how can we know?