This lecture and tutorial focuses on measuring human functional brain networks. The lecture and tutorial were part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Lecture on functional brain parcellations and a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation which were part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Neuronify is an educational tool meant to create intuition for how neurons and neural networks behave. You can use it to combine neurons with different connections, just like the ones we have in our brain, and explore how changes on single cells lead to behavioral changes in important networks. Neuronify is based on an integrate-and-fire model of neurons. This is one of the simplest models of neurons that exist. It focuses on the spike timing of a neuron and ignores the details of the action potential dynamics. These neurons are modeled as simple RC circuits. When the membrane potential is above a certain threshold, a spike is generated and the voltage is reset to its resting potential. This spike then signals other neurons through its synapses.

Neuronify aims to provide a low entry point to simulation-based neuroscience.

Overview of the content for Day 1 of this course.

Overview of Day 2 of this course.

Best practices: the tips and tricks on how to get your Miniscope to work and how to get your experiments off the ground.

"Faster & more sensitive imaging with the MiniFAST" was presented by Caleb Kemere at the 2021 Virtual Miniscope Workshop as part of a series of talks by leading Miniscope users and developers.

"Balancing size & function in compact miniscopes" was presented by Tycho Hoogland at the 2021 Virtual Miniscope Workshop as part of a series of talks by leading Miniscope users and developers.

"Computational imaging for miniature miniscopes" was presented by Laura Waller at the 2021 Virtual Miniscope Workshop as part of a series of talks by leading Miniscope users and developers.

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.

"Online 1-photon vs 2-photon calcium imaging data analysis: Current developments and future plans" was presented by Andrea Giovannucci at the 2021 Virtual Miniscope Workshop as part of a series of talks by leading Miniscope users and developers.

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.

"Ensemble fluidity supports memory flexibility during spatial reversal" was presented by William Mau at the 2021 Virtual Miniscope Workshop as part of a series of talks by leading Miniscope users and developers.

How to start processing the raw imaging data generated with a Miniscope, including developing a usable pipeline and demoing the Minion pipeline

The direction of miniature microscopes, including both MetaCell and other groups.