Let's consider that an M-model transmits no information (e.g. our eyes are
closed). A local-ZM takes the information from that M-model. Because the M-
model transmits no information, the ZM must receive no information. What is
really received is called NULL-model. For a normal brain, in the above
condition, the local-ZM must receive a completely dark surface. What is really
received is an indication about the overall status of the brain.
For instance, in the above conditions, we can receive a dark surface with some
randomly moving points. That is, the local-ZM detects a bright point in a
place, but at the second scan the point is not there anymore. This means that
there is a noise, but no important hardware problems. A stable image is
generated by a hardware problem of M or ZM models.
Application: In the first seconds after wake up, with closed eyes, look
towards a moderately bright surface. Usually, one should perceive a dark
surface full of grey points moving randomly. After a few seconds, the surface
becomes a uniform dark-grey one. This is a typical situation for a brain in a
normal status.
It is also possible, in the first moments, to see big bright points or shapes,
moving randomly. They evolve to dark and small grey points, and then to a
uniform grey surface. In such a situation, the brain is not in a good shape
(maybe the person did not sleep enough...)
Anyways, if the final status of the NULL model is a uniform grey surface, the
brain is OK.
This case has been illustrated for the eyes, but NULL models exist for all
senses.