  The paper describes a program which computes the best possible Bayesian model
of 3D space from vision (in bees) or echo location (in bats), at Marrs [1982]
Level 2. The model exploits the strong Bayesian prior probability that most
other things do not move, as the animal moves. 3D locations of things are
computed from successive sightings or echoes, computing structure from the
animals motion (SFM). The program can be downloaded and run. It also computes a
tracking approximate model, which is more tractable for animal brains than the
full Bayesian computation. The tracking model is nearly as good as the full
Bayesian model, but only if spatial memory storage errors are small. Neural
storage of spatial positions gives too high error levels, and is too slow.
Alternatively, a 3D model of space could be stored in a wave excitation, as a
Fourier transform of real space. This could give high memory capacity and
precision, with low spatial distortion, fast response, and simpler computation.
Evidence is summarized from related papers that a wave excitation holds spatial
memory in the mammalian thalamus, and in the central body of the insect brain.
