This lesson is an overview of transcriptomics, from fundamental concepts of the central dogma and RNA sequencing at the single-cell level, to how genetic expression underlies diversity in cell phenotypes. 

This lesson delves into the the structure of one of the brain's most elemental computational units, the neuron, and how said structure influences computational neural network models. 

This lesson goes over the basic mechanisms of neural synapses, the space between neurons where signals may be transmitted. 

While the previous lesson in the Neuro4ML course dealt with the mechanisms involved in individual synapses, this lesson discusses how synapses and their neurons' firing patterns may change over time. 

Whereas the previous two lessons described the biophysical and signalling properties of individual neurons, this lesson describes properties of those units when part of larger networks. 

As the previous lesson of this course described how researchers acquire neural data, this lesson will discuss how to go about interpreting and analysing the data. 

This lecture provides an introduction to the Brain Imaging Data Structure (BIDS), a standard for organizing human neuroimaging datasets.