I assume that human brains can be considered as directed graphs with neurons as nodes and synapses as edges. I explicitly **don't** want to consider the *weights*, the *dynamics* of neural activity (based on the weights), and the adjustment of weights (*learning*) - just brains as static unweighted finite directed graphs.

Sensor neurons may be those having in-degree 0, actor neurons may be those having out-degree 0. (0 meaning "*essentially* 0".)

Considering human brains as finite directed graphs, for each question concerning finite directed graphs there should be an answer with respect to human brains.

Such questions might be:

- How long is the *shortest* path from a sensor to an actor neuron?

- How long is the *longest* (direct) path from a sensor to an actor neuron?

- What is the (global/local) *layer* structure (on different levels of granularity)?

- What is the (global/local) *cycle* structure (on different levels of granularity)?

I find it hard to get answers to such questions considering human brains as directed graphs, because neuro-scientists don't think in terms of graphs, but for example in terms of signal paths and neuro-anatomy. But then - for them - "anything goes", and "everything is connected to everything" - which is not very helpful.

> I would be very glad for any reference treating (formally) human
> brains as directed graphs.