Neuromodulation refers to devices that influence the firing of neurons which can be useful in many medical applications. This modules covers what neuromodulation is, how it affects the functioning of neurons, and the many forms that these devices take on.

This modules covers neuroprosthetic and cognitive enhancement devices that can help augment our capabilities by enhancing memory, as well as restoring or improving our senses. 

This module goes over the methods that neurotechnologists use to turn brain data into commands a computer or a machine can understand. We cover data collection, processing, filtering, analysis, how to generate an action in a device, asynchronous BCIs that use population encoding, and synchronous BCIs that use P300, SSVEP, N100, and N400 signals. 

This module covers the many things that brain-computer interfaces can and will be able to do, including motor neuroprosthetics like prosthetic arms, exosuits, and vehicle control, as well as computer and machine interfacing use-cases.

This module covers how neurotechnology is perceived in media today. We discuss a few specific films and talk about how the perception of neurotechnology changes with our media. Finally, we introduce a few interesting terms related to ethics and address some future issues the technology may cause.

What will happen to the mind and our personalities when we start modifying our brains and bodies with technology? What is the mind and how should we think about it? What is a cyborg and what makes them human? Where is the line between these? This video invites us to think about what the future of consciousness might look like. 

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

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

This lecture covers the description and characterization of an input-output relationship in a information-theoretic context. 

This lesson is part 1 of 2 of a tutorial on statistical models for neural data.

This lesson is part 2 of 2 of a tutorial on statistical models for neural data.

This lesson provides an introduction to modeling single neurons, as well as stability analysis of neural models.

This lesson continues a thorough description of the concepts, theories, and methods involved in the modeling of single neurons. 

In this lesson you will learn about fundamental neural phenomena such as oscillations and bursting, and the effects these have on cortical networks. 

This lesson continues discussing properties of neural oscillations and networks. 

In this lecture, you will learn about rules governing coupled oscillators, neural synchrony in networks, and theoretical assumptions underlying current understanding.

This lesson provides a continued discussion and characterization of coupled oscillators. 

This lesson gives an overview of modeling neurons based on firing rate. 

This lesson characterizes the pattern generation observed in visual system hallucinations.

This lesson gives an introduction to stability analysis of neural models.