One major challenge of neuroscience is finding interesting structures in a
seemingly disorganized neural activity. Often these structures have
computational implications that help to understand the functional role of a
particular brain area. Here we outline a unified approach to characterize these
structures by inspecting the representational geometry and the modularity
properties of the recorded activity, and show that this approach can also
reveal structures in connectivity. We start by setting up a general framework
for determining geometry and modularity in activity and connectivity and
relating these properties with computations performed by the network. We then
use this framework to review the types of structure found in recent works on
model networks performing three classes of computations