Data Models
Most neuroscience data ends up as a multidimensional array. To analyse it correctly you also need to know what each axis means, what physical units the values carry, and how signals relate to one another. This unit builds from raw arrays to rich, self-describing data models for electrophysiology. The homework session introcues the NumPy and Xarray array libraries, while the in the two in-class sessions on Neo, you’ll learn how to use Neo to represent continuous signals, spikes, events, and epochs, organise them into trial-based datasets, as well as to select, cut, and visualise the data.
Tools
Sessions
Numpy and Xarray
Working with labelled multidimensional arrays for neuroscience data.
Organizing Electrophysiology Data
Use Neo to read and write electrophysiological data.
Representing Electrophysiological Data with Neo
Physical quantities, AnalogSignal, SpikeTrain, Events, Epochs, and Annotations.