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

This lesson introduces the practical exercises which accompany the previous lessons on animal and human connectomes in the brain and nervous system. 

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. 

This lesson describes spike timing-dependent plasticity (STDP), a biological process that adjusts the strength of connections between neurons in the brain, and how one can implement or mimic this process in a computational model. You will also find links for practical exercises at the bottom of this page. 

 In this lesson, you will learn about some of the many methods to train spiking neural networks (SNNs) with either no attempt to use gradients, or only use gradients in a limited or constrained way. 

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

Explore how to setup an epileptic seizure simulation with the TVB graphical user interface. This lesson will show you how to program the epileptor model in the brain network to simulate a epileptic seizure originating in the hippocampus. It will also show how to upload and view mouse connectivity data, as well as give a short introduction to the python script interface of TVB.

This lesson describes how DataLad allows you to track and mange both your data and analysis code, thereby facilitating reliable, reproducible, and shareable research.

This tutorial covers the fundamentals of collaborating with Git and GitHub.

This lecture and tutorial focuses on measuring human functional brain networks, as well as how to account for inherent variability within those networks. 

This lesson provides an overview of Jupyter notebooks, Jupyter lab, and Binder, as well as their applications within the field of neuroimaging, particularly when it comes to the writing phase of your research. 

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 lecture gives an overview of how to prepare and preprocess neuroimaging (EEG/MEG) data for use in TVB.  

This lesson provides a brief introduction to the Computational Modeling of Neuronal Plasticity.

In this lesson, you will be introducted to a type of neuronal model known as the leaky integrate-and-fire (LIF) model.

This lesson goes over various potential inputs to neuronal synapses, loci of neural communication.

This lesson describes the how and why behind implementing integration time steps as part of a neuronal model.

In this lesson, you will learn about neural spike trains which can be characterized as having a Poisson distribution.

This lesson covers spike-rate adaptation, the process by which a neuron's firing pattern decays to a low, steady-state frequency during the sustained encoding of a stimulus.