This chapter introduces data obtained from recordings of individual neurons, which we typically call single unit data (“units” being neurons). Single unit data is typically analyzed in terms of spike trains — sequences of action potentials. In this chapter we will learn how spike trains can be represented as data, and some ways of working with and visualizing them. As well, the chapter extends our knowledge of Python, including working with NumPy arrays, and complex figures with multiple subplots, as well as further discussion of the use of colour and other considerations in scientific visualization. Spike trains
-
define spike trains
-
explain how spike train data is recorded
-
describe two ways of storing spike train data: time series and spike times
-
generate two type of visualizations of spike train data: raster plots and peri-stimulus time histograms (PSTHs)
-
interpret raster plots and PSTHs with respect to experimental manipulations
-
generate 2D heat maps of PSTHs
-
work with data sets comprising thousands of rows
-
generate correlation matrices of spike train data from multi-unit recordings
Python
-
create and work with data in lists, NymPy arrays, and pandas DataFrames
-
use nested list comprehension
-
use subplots to plot multiple levels of data in a single graphic
-
generate 2D images from data
Data Visualization
- make informed decisions about accessible design in scientific visualization, including colour map choice and interpolation methods