In this lesson, you will learn in more detail about neuromorphic computing, that is, non-standard computational architectures that mimic some aspect of the way the brain works.
This video provides a very quick introduction to some of the neuromorphic sensing devices, and how they offer unique, low-power applications.
This lesson provides an introduction to the lifecycle of EEG/ERP data, describing the various phases through which these data pass, from collection to publication.
In this lesson you will learn about experimental design for EEG acquisition, as well as the first phases of the EEG/ERP data lifecycle.
This lesson provides an overview of the current regulatory measures in place regarding experimental data security and privacy.
In this lesson, you will learn the appropriate methods for collection of both data and associated metadata during EEG experiments.
This lesson goes over methods for managing EEG/ERP data after it has been collected, from annotation to publication.
In this final lesson of the course, you will learn broadly about EEG signal processing, as well as specific applications which make this kind of brain signal valuable to researchers and clinicians.
This lecture introduces neuroscience concepts and methods such as fMRI, visual respones in BOLD data, and the eccentricity of visual receptive fields.
This tutorial walks users through the creation and visualization of activation flat maps from fMRI datasets.
This tutorial demonstrates to users the conventional preprocessing steps when working with BOLD signal datasets from fMRI.
In this tutorial, users will learn how to create a trial-averaged BOLD response and store it in a matrix in MATLAB.
This tutorial teaches users how to create animations of BOLD responses over time, to allow researchers and clinicians to visualize time-course activity patterns.
This tutorial demonstrates how to use MATLAB to create event-related BOLD time courses from fMRI datasets.
In this tutorial, users learn how to compute and visualize a t-test on experimental condition differences.
This lesson introduces various methods in MATLAB useful for dealing with data generated by calcium imaging.
This tutorial demonstrates how to use MATLAB to generate and visualize animations of calcium fluctuations over time.
This tutorial instructs users how to use MATLAB to programmatically convert data from cells to a matrix.
In this tutorial, users will learn how to identify and remove background noise, or "blur", an important step in isolating cell bodies from image data.
This lesson teaches users how MATLAB can be used to apply image processing techniques to identify cell bodies based on contiguity.