Two useful concepts from signal processing are in fact familiar. One is "signal-to-noise ratio," the other "dimensionality reduction."
Signal to noise ratio (or S/N for short) is the quantification of the fact, obvious to anyone who has dialed a radio, that the crucial listening quality is not the loudness of the signal on its own, nor the background noise, but the relationship between the two. Signal-to-noise matters because real brains suffer from it horrifically, at the billions-fold level if one considers vision in dim light, yet manage to function nonetheless.
Dimensionality reduction is how one reduces noise. The best-known example is averaging many numbers (the high-dimensional source) into a single (low-dimensional) number, like the grade-point averages used in education. But one can also do that averaging in more complex ways, such as averaging by day, month, subject, or trend.
A human brain must consume at least a million-dimensional sensory signal, and from it must construct a model of a continuous 3-D world. This suggests that a brain reduces its dimensionality from millions down to three (or four, if one counts time). Every technology I know which efficiently represents a 3-D world--including physics simulations, StreetView, robots, and self-driving cars--at some point merges all its data into a single 3-D form. A brain should do no less.