This module introduces computational neuroscience by simulating neurons according to the AdEx model. You will learn about generative modeling, dynamical systems, and FI curves. The MATLAB code introduces Live Scripts and functions.

This module introduces computational neuroscience by simulating neurons according to the AdEx model. You will learn about generative modeling, dynamical systems, and FI curves. The MATLAB code introduces Live Scripts and functions.

This module covers fMRI data, including creating and interpreting flat maps, exploring variability and average responses, and visual eccentricity. You will learn about processing BOLD signals, trial-averaging, and t-tests. The MATLAB code introduces data animations, multicolor visualizations, and linear indexing.

This module covers fMRI data, including creating and interpreting flatmaps, exploring variability and average responses, and visual eccentricity. You will learn about processing BOLD signals, trial-averaging, and t-tests. The MATLAB code introduces data animations, multicolor visualizations, and linear indexing.

This module covers fMRI data, including creating and interpreting flatmaps, exploring variability and average responses, and visual eccentricity. You will learn about processing BOLD signals, trial-averaging, and t-tests. The MATLAB code introduces data animations, multicolor visualizations, and linear indexing.

This module covers fMRI data, including creating and interpreting flatmaps, exploring variability and average responses, and visual eccentricity. You will learn about processing BOLD signals, trial-averaging, and t-tests. The MATLAB code introduces data animations, multicolor visualizations, and linear indexing.

This module covers fMRI data, including creating and interpreting flatmaps, exploring variability and average responses, and visual eccentricity. You will learn about processing BOLD signals, trial-averaging, and t-tests. The MATLAB code introduces data animations, multicolor visualizations, and linear indexing.

This module covers fMRI data, including creating and interpreting flatmaps, exploring variability and average responses, and visual eccentricity. You will learn about processing BOLD signals, trial-averaging, and t-tests. The MATLAB code introduces data animations, multicolor visualizations, and linear indexing.

This module covers fMRI data, including creating and interpreting flatmaps, exploring variability and average responses, and visual eccentricity. You will learn about processing BOLD signals, trial-averaging, and t-tests. The MATLAB code introduces data animations, multicolor visualizations, and linear indexing.

You will learn about working with calcium imaging data, including image processing to remove background "blur," identifying cells based on thresholded spatial contiguity, time series filtering, and principal components analysis (PCA). The MATLAB code shows data animations, capabilities of the image processing toolbox, and PCA.

You will learn about working with calcium imaging data, including image processing to remove background "blur," identifying cells based on thresholded spatial contiguity, time series filtering, and principal components analysis (PCA). The MATLAB code shows data animations, capabilities of the image processing toolbox, and PCA.

You will learn about working with calcium imaging data, including image processing to remove background "blur," identifying cells based on thresholded spatial contiguity, time series filtering, and principal components analysis (PCA). The MATLAB code shows data animations, capabilities of the image processing toolbox, and PCA.

You will learn about working with calcium imaging data, including image processing to remove background "blur," identifying cells based on thresholded spatial contiguity, time series filtering, and principal components analysis (PCA). The MATLAB code shows data animations, capabilities of the image processing toolbox, and PCA.

You will learn about working with calcium imaging data, including image processing to remove background "blur," identifying cells based on thresholded spatial contiguity, time series filtering, and principal components analysis (PCA). The MATLAB code shows data animations, capabilities of the image processing toolbox, and PCA.

You will learn about working with calcium imaging data, including image processing to remove background "blur," identifying cells based on thresholded spatial contiguity, time series filtering, and principal components analysis (PCA). The MATLAB code shows data animations, capabilities of the image processing toolbox, and PCA.

You will learn about working with calcium imaging data, including image processing to remove background "blur," identifying cells based on thresholded spatial contiguity, time series filtering, and principal components analysis (PCA). The MATLAB code shows data animations, capabilities of the image processing toolbox, and PCA.

This tutorial was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Introduction to the central concepts of machine learning, and how they can be applied in Python using the Scikit-learn Package. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

This book was written with the goal of introducing researchers and students in a variety of research fields to the intersection of data science and neuroimaging. This book reflects our own experience of doing research at the intersection of data science and neuroimaging and it is based on our experience working with students and collaborators who come from a variety of backgrounds and have a variety of reasons for wanting to use data science approaches in their work. The tools and ideas that we chose to write about are all tools and ideas that we have used in some way in our own research. Many of them are tools that we use on a daily basis in our work. This was important to us for a few reasons: the first is that we want to teach people things that we ourselves find useful. Second, it allowed us to write the book with a focus on solving specific analysis tasks. For example, in many of the chapters you will see that we walk you through ideas while implementing them in code, and with data. We believe that this is a good way to learn about data analysis, because it provides a connecting thread from scientific questions through the data and its representation to implementing specific answers to these questions. Finally, we find these ideas compelling and fruitful. That’s why we were drawn to them in the first place. We hope that our enthusiasm about the ideas and tools described in this book will be infectious enough to convince the readers of their value.

This lecture focuses on ontologies for clinical neurosciences.