Model-based spatial navigation in the hippocampus-ventral striatum circuit: A computational analysis
Fig 4
Neural representation of state transitions in the state-state model.
Latent states developed by the state-transition models averaged across all 10 learners after the Cue Conditioning phase (A); and the changes due to Contextual Conditioning, i.e., the differences between probabilities before and after Contextual Conditioning (B). Each image from the transition model PM(s′|s,a) encodes the greatest likelihoods PM(xy(s′)|xy(s), a) across all head-directions and actions to step from the location xy(s) of a given latent state (place) s to nearby places xy(s′) located within the range of an(y) action from the location of the current place, following any of the available actions, i.e., the (probabilistic) location of the successors of every state. The locations xy(s) and xy(s′) of s and s′ are decoded using an inverse of the function providing input to the Dirichlet model. Note that, as expected, the decoding procedure is not perfect—hence the gaps in the maps.