This video provides a very quick introduction to some of the neuromorphic sensing devices, and how they offer unique, low-power applications.

This lecture covers the linking neuronal activity to behavior using AI-based online detection. 

This lesson contains practical exercises which accompanies the first few lessons of the Neuroscience for Machine Learners (Neuro4ML) course. 

This lesson introduces some practical exercises which accompany the Synapses and Networks portion of this Neuroscience for Machine Learners course. 

In this lesson, you will learn how to train spiking neural networks (SNNs) with a surrogate gradient method. 

This video briefly goes over the exercises accompanying Week 6 of the Neuroscience for Machine Learners (Neuro4ML) course, Understanding Neural Networks.

This lesson gives an introduction to the central concepts of machine learning, and how they can be applied in Python using the scikit-learn package. 

This lesson provides a hands-on, Jupyter-notebook-based tutorial to apply machine learning in Python to brain-imaging data.

This lesson from freeCodeCamp introduces Scikit-learn, the most widely used machine learning Python library.

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.

The Allen Mouse Brain Atlas is a genome-wide, high-resolution atlas of gene expression throughout the adult mouse brain. This tutorial describes the basic search and navigation features of the Allen Mouse Brain Atlas.

The Allen Developing Mouse Brain Atlas is a detailed atlas of gene expression across mouse brain development. This tutorial describes the basic search and navigation features of the Allen Developing Mouse Brain Atlas.

This tutorial demonstrates how to use the differential search feature of the Allen Mouse Brain Atlas to find gene markers for different regions of the brain, as well as to visualize this gene expression in three-dimensional space. Differential search is also available for the Allen Developing Mouse Brain Atlas and the Allen Human Brain Atlas.

This lecture provides an introduction to optogenetics, a biological technique to control the activity of neurons or other cell types with light.

This lecture provides an introduction to the study of eye-tracking in humans. 

This is a tutorial on designing a Bayesian inference model to map belief trajectories, with emphasis on gaining familiarity with Hierarchical Gaussian Filters (HGFs).

This lesson corresponds to slides 65-90 of the PDF below. 

This demonstration walks through how to import your data into MATLAB.

This lesson provides instruction regarding the various factors one must consider when preprocessing data, preparing it for statistical exploration and analyses. 

This tutorial outlines, step by step, how to perform analysis by group and how to do change-point detection.

This tutorial walks through several common methods for visualizing your data in different ways depending on your data type.