Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees. It is appropriate for student population ranging from undergraduates to faculty in academic settings and also includes industry professionals. In addition to teaching the technical details of computational methods, Neuromatch Academy also provide a curriculum centered on modern neuroscience concepts taught by leading professors along with explicit instruction on how and why to apply models. 

This course focuses on the purpose of model fitting, approaches to model fitting, model fitting for linear models, and how to assess the quality and compare model fits, as well as a 10-step practical guide on how to succeed in modeling. 

Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees. It is appropriate for student population ranging from undergraduates to faculty in academic settings and also includes industry professionals. In addition to teaching the technical details of computational methods, Neuromatch Academy also provide a curriculum centered on modern neuroscience concepts taught by leading professors along with explicit instruction on how and why to apply models. 

This course on model types introduces the advantages of modeling, provide examples of different model types, and explain what modeling is all about, as well as how models can be used answer different scientific questions.

Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees. It is appropriate for student population ranging from undergraduates to faculty in academic settings and also includes industry professionals. In addition to teaching the technical details of computational methods, Neuromatch Academy also provide a curriculum centered on modern neuroscience concepts taught by leading professors along with explicit instruction on how and why to apply models. 

This course focuses on how to get from a scientific question to a model using concrete examples, presenting a 10-step practical guide on how to succeed in modeling.

Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees. It is appropriate for student population ranging from undergraduates to faculty in academic settings and also includes industry professionals. In addition to teaching the technical details of computational methods, Neuromatch Academy also provide a curriculum centered on modern neuroscience concepts taught by leading professors along with explicit instruction on how and why to apply models. This particular course provides an overview of generalized linear models (GLMs), and how they are relevant when applying machine learning algorithms to data. 

Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees. It is appropriate for student population ranging from undergraduates to faculty in academic settings and also includes industry professionals. In addition to teaching the technical details of computational methods, Neuromatch Academy also provide a curriculum centered on modern neuroscience concepts taught by leading professors along with explicit instruction on how and why to apply models. 

This course introduces the core concepts of dimensionality reduction and provides an application of dimensionality reduction applied to multi-dimensional neural recordings using brain-computer interfaces with simultaneous spike recordings.

Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees. It is appropriate for student population ranging from undergraduates to faculty in academic settings and also includes industry professionals. In addition to teaching the technical details of computational methods, Neuromatch Academy also provide a curriculum centered on modern neuroscience concepts taught by leading professors along with explicit instruction on how and why to apply models. 

This course introduces the concept of Bayesian statistics and explains why Bayesian statistics are relevant to studying the brain.

Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees. It is appropriate for student population ranging from undergraduates to faculty in academic settings and also includes industry professionals. In addition to teaching the technical details of computational methods, Neuromatch Academy also provide a curriculum centered on modern neuroscience concepts taught by leading professors along with explicit instruction on how and why to apply models. 

This course on introduces the dynamic features of the brain and their allied dynamical systems description including the twin perspectives from dynamical systems: geometric and algorithmic/computational. The course also covers linear dynamical systems in terms of approximations near equilibria and the usefulness of this approximation is in terms of geometrical approaches (eigenvector decompositions), including how this leads to line attractors and models of optimal decisions as well as “non-normal” surprises when eigenvectors are not orthogonal, as well as the treatment of time-dependent systems, including those driven by stochastic signals or noise, and close with examples on how networks and the activity that they produce co-evolve over time.

Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees. It is appropriate for student population ranging from undergraduates to faculty in academic settings and also includes industry professionals. In addition to teaching the technical details of computational methods, Neuromatch Academy also provide a curriculum centered on modern neuroscience concepts taught by leading professors along with explicit instruction on how and why to apply models.

This course introduces the "hidden states" that neurons and networks have that affect their function and how to use graphical models with hidden states to learn about the dynamics in the world that we only have access to from noisy measurements. In addition, the course introduces multiple topics on dynamical neural modeling and inference and their application to basic neuroscience and neurotechnology design:

• How to develop multiscale dynamical models and filters?
• How to study neural dynamics across spatiotemporal scales?
• How to dissociate and model behaviorally relevant neural dynamics?
• How to model neural dynamics in response to electrical stimulation input?
• How to apply these techniques in developing brain-machine interfaces (BMIs) to restore lost motor or emotional function?

Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees. It is appropriate for student population ranging from undergraduates to faculty in academic settings and also includes industry professionals. In addition to teaching the technical details of computational methods, Neuromatch Academy also provide a curriculum centered on modern neuroscience concepts taught by leading professors along with explicit instruction on how and why to apply models.

This course provides an introduction of optimal control, describes open-loop and closed-loop control, and application to motor control.

Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees. It is appropriate for student population ranging from undergraduates to faculty in academic settings and also includes industry professionals. In addition to teaching the technical details of computational methods, Neuromatch Academy also provide a curriculum centered on modern neuroscience concepts taught by leading professors along with explicit instruction on how and why to apply models.

This course provides an introduction to the features of a Reinforcement Learning (RL) system, general methods for predicting state values, an overview of the control problem in RL, and brief introduction to function approximation and deep RL.