Figure 1.
Architecture of the integrated model that combines both navigation strategies.
It combines a Cue-based and a Place-based navigation module. Each module consists of a unique Critic and a Temporal Difference error signal. Whereas the Cue-based module depends on the visible target, the Place-based module depends on the spatial context. One of the modules is selected at any given instant, by comparing the values estimated by the two modules.
Figure 2.
View-vector construction in case of cue-based navigation.
The platform is the smaller circle to the right of the circular pool. (The black dots on the rim are poles used for place-based navigation. Not all poles are shown.) The field of view (angle of vision = 180°) is divided into 30 sectors. Sectors that intersect with the platform are associated with a value of 1; those that do not intersect are given a value of 0. Thus a 30-dimensional binary Cue-based Visual Input Vector is constructed.
Figure 3.
Architecture of the module for cue-based navigation.
The striatum receives dopamine signal from SNc, and the sensory input Cue-based Visual Input Vector, which is processed by Direct Pathway and Indirect Pathway. Value is computed in striatum. Outputs of Direct Pathway and Indirect Pathway are combined to compute the action, which represents the displacement (Δz = [Δx, Δy]) of the simulated rat in the next step.
Figure 4.
Architecture of the place-based module.
The striatum receives dopamine signal from SNc, and the sensory state from Hippocampus, which is processed by Direct Pathway and Indirect Pathway. Value is computed in striatum. Outputs of Direct Pathway and Indirect Pathway are combined to compute the action, which represents the displacement (Δz = [Δx, Δy]) of the simulated rat.
Figure 5.
Representation of the modules that constitute the hippocampus.
Visual input from the spatial context is presented to the SOM. Output of the SOM is the input to the CANN. CANN output is presented as input to the Basal ganglia in the place-based module.
Figure 6.
(a) A snapshot of the SOM response for a given Context-based Visual Input Vector. (b) The corresponding CANN response for the same Context-based Visual Input Vector.
Figure 7.
Critic profiles obtained on (a)cue-based module on Day 1, (b) cue-based module on Day 2, (c) place-based module on Day 3, (d) cue-based module on Day 4, (e) cue-based module on Day 5, (f) place-based module on Day 6, (g) cue-based module on Day 7, (h) cue-based module on Day 8, and (i) place-based module on Day 9.
Note that both place-based and cue-based modules are trained on days 1,2,4,5,7,8, though only value profiles of cue-based module alone are shown. The value for both modules is a function of a high-dimensional vector. For ease of presentation, the value show in the above plot corresponds to a given position of the simulated rat, when the rat is oriented towards the center of the platform.
Figure 8.
(a) Comparison of the escape latency of the agent in the experimental set-up, shown in seconds [1], and that of the simulated rat on different days of training shown as number of steps to reach the platform. (b) The hit rate of the model rat (expressed as percentage) on the different days of training.
Figure 9.
(a) Sample trajectory of the model rat going to the old location of the platform due to dominance of place-based trajectory on the 10th day. (b) A sample trajectory in which the model rat first goes towards the old platform and then goes to the new platform location on the 10th day.
Figure 10.
Sample trajectory of the simulated rat when only cue-based response assists navigation.
Dotted circle refers to the previous location of the platform, while the solid circle on the left denotes the current location.
Figure 11.
Average number of steps as a function of percentage Dopamine cell loss in PD model rat.
Figure 12.
Hit rate as a function of percentage Dopamine cell loss in PD model rat.
Figure 13.
A sample trajectory of the PD model rat to reach the platform for % Dopamine loss = 50.
The model rat's movements are confined to a small part of the pool, and show no consistent progression towards the platform.
Figure 14.
Schematic depicting a hypothetical, expanded view of the roles of basal ganglia and hippocampus to spatial navigation.
In this view, both basal ganglia and hippocampus are capable of computing their own unique value functions by combining the respective sensory states accessible by them, and the dopamine projections from midbrain dopamine centers. Navigation subserved by basal ganglia based on visuospatial information is cue-based navigation. Navigation subserved by basal ganglia based on visuospatial information is S-R type navigation. Navigation subserved by hippocampus based on visuospatial information is place-based navigation. Navigation subserved by hippocampus based on proprioceptive information is path-integration.