This is a tutorial on how to simulate neuronal spiking in brain microcircuit models, as well as how to analyze, plot, and visualize the corresponding data. 

This video will document the process of running an app on brainlife, from data staging to archiving of the final data outputs.

This quick video presents some of the various visualizers available on brainlife.io

This short video shows how a brainlife.io publication can be opened from the Data Deposition page of the journal Nature Scientific Data.

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 covers the history of behaviorism and the ultimate challenge to behaviorism. 

This lecture covers various learning theories.

This lesson characterizes different types of learning in a neuroscientific and cellular context, and various models employed by researchers to investigate the mechanisms involved. 

In this lesson, you will learn about different approaches to modeling learning in neural networks, particularly focusing on system parameters such as firing rates and synaptic weights impact a network. 

How does the brain learn? This lecture discusses the roles of development and adult plasticity in shaping functional connectivity.

While the previous lesson in the Neuro4ML course dealt with the mechanisms involved in individual synapses, this lesson discusses how synapses and their neurons' firing patterns may change over time. 

In this lesson, you will learn about how machine learners and computational neuroscientists design and build models of neuronal synapses. 

This lesson goes into the mechanisms behind changes in synaptic function created by learning.