This module covers many of the types of non-invasive neurotech and neuroimaging devices including electroencephalography (EEG), electromyography (EMG), electroneurography (ENG), magnetoencephalography (MEG), and more.
An introduction to data management, manipulation, visualization, and analysis for neuroscience. Students will learn scientific programming in Python, and use this to work with example data from areas such as cognitive-behavioral research, single-cell recording, EEG, and structural and functional MRI. Basic signal processing techniques including filtering are covered. The course includes a Jupyter Notebook and video tutorials.
This lecture and tutorial focuses on measuring human functional brain networks, as well as how to account for inherent variability within those networks.
This lecture presents an overview of functional brain parcellations, as well as a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation.
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.