Forward and Backward Inference in Spatial Cognition
Figure 8
Here indexes time and we have control signals
, path integral hidden state estimates
, Bayesian state estimates,
, non-spatial sensory states,
and predictions of non-spatial sensory states
. During Localisation, path integration in MEC combines previous state estimates and motor efference copy to produce a new state estimate, with mean
as described in equation 23. Bayesian inference in CA3-CA1 combines path integration with sensory input to get an improved state estimate
as described in equation 24. LEC sends a prediction error signal
to CA3-CA1. The computations underlying ‘sensory imagery’, ‘decision making’ and ‘model selection’ are discussed in the main text in the section on ‘Neural Implementation’. CA: Cornu Ammonis, LEC/MEC: Lateral/Medial Entorhinal cortex.