Hi,
Here is a competing hypothesis about how to implement temporal stability:
Learning Invariance from Transformation Sequences
Peter Foldiak, 1991
Physiological Laboratory, University of Cambridge
https://www.researchgate.net/publication/215991433_Learning_Invariance_From_Transformation_Sequences
Foldiak proposes a new learning rule:
“A trace is a running average of the activation of the unit, which has the effect that activity at one moment will influence learning at a later moment. This temporal low-pass filtering of the activity embodies the assumption that the desired features are stable in the environment.”
Also, Kropff & Treves used a very similar mechanism in their model of grid cells, which I discuss in detail in a 20 minute recorded lecture: