  Researchers continue exploring neurons' intricate patterns of activity in the
cerebral visual cortex in response to visual stimuli. The way neurons
communicate and optimize their interactions with each other under different
experimental conditions remains a topic of active investigation. Probabilistic
Graphical Models are invaluable tools in neuroscience research, as they let us
identify the functional connections, or conditional statistical dependencies,
between neurons. Graphical models represent these connections as a graph, where
nodes represent neurons and edges indicate the presence of functional
connections between them. We developed the R package neurofuncon for the
computation and visualization of functional connectivity graphs from
large-scale data based on the Graphical lasso. We illustrate the use of this
package with publicly available two-photon calcium microscopy imaging data from
approximately 10000 neurons in a 1mm cubic section of a mouse visual cortex.
