Three Dimensional Spatial Cognition: Bees and Bats

TL;DR

This paper develops Bayesian models for 3D spatial cognition in bees and bats, comparing full Bayesian and tracking approximations, and explores neural wave-based storage for efficient spatial memory.

Contribution

It introduces a Bayesian framework for 3D space perception in animals and proposes a wave excitation model for neural spatial memory storage.

Findings

Tracking model nearly matches full Bayesian accuracy with low memory errors

Neural storage of spatial positions has high error and slow response

Wave excitation can store spatial memory efficiently with high capacity

Abstract

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.