A) Evidence from fMRI adaptation. When viewing images of landmarks from a familiar college campus, fMRI activity in the left hippocampus scales with the real-world distance between the landmark shown on each trial and the landmark shown on the immediately preceding trial (adapted from ref. 25). B) Evidence from multi-voxel pattern analysis (MVPA). Voxelwise activity patterns in the hippocampus reflect distances between events intermittently logged by a camera worn by participants in the 30 days prior to the scan (aerial map of navigated territory shown on the left, as well as example pictures; adapted from ref. 28). C) Evidence from an encoding model. Participants performed a virtual reality navigation task. Grid cells in an individual rat all have the same orientation (; top row), and thus it was predicted that movements aligned with the grid orientation should result in more fMRI activity than movements misaligned with the grid. The expected pattern of results was observed in human entorhinal cortex (EC, bottom row; adapted from ref. 29)
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The Map is a Mechanic that can be used to navigate the world, locate quests, place markers as perform various other useful functions.
It can be currently activated through the Keybind M.
Scenes represent the areas of a World that the players may explore. Scenes may depict a variety of settings ranging from world or regional maps all the way down to small buildings or dungeons. At each point in time, one Scene is classified as the active scene. The same scene is active for all users. For each individual user a different Scene may be treated as the viewed scene, which is the area currently rendered on the game canvas for that user.
The Scene configuration menu allows you to customize the structure and appearance of each area within your world. This configuration is displayed automatically when a new Scene is created, but can always be accessed by left-clicking on the Scene in the sidebar directory, or by right clicking on the Scene in the top navigation bar and selecting Configure.
The enhanced performance was a result of better map learning as well as better navigation. To understand the former, consider Figure 7E: Here the agent has begun to learn the ring graph by walking back and forth between a few nodes (2-5), thus establishing all their pairwise map synapses; then it steps to a new node (6). With a linear activation function (Fig 7E left), the recurrent synapses enhance the map output, so the map signal with the agent in the explored region (2-5) is considerably larger than after stepping to the new node. This interferes with the mechanism for map learning: The learning rule must identify which of the map cells represents the current location of the agent, and does so by setting a threshold on the output signal (Alg 2). In the present example, this leads to erroneous synapses, because a map cell that receives only recurrent input (4) produces outputs larger than the threshold (arrowhead in Fig 7E). With the saturating activation function (Fig 7E right), the directly activated map cells always have the largest output signal, so the learning rule can operate without errors.
Once the animal gains familiarity with the environment, it performs fewer of the vicarious trial and error movements, and instead moves smoothly through multiple intersections in a row [48]. This may reflect a transition between different modes of navigation, from the early endotaxis, where every action gets evaluated on its real-world merit, to a mode where many actions are strung together into behavioral motifs. Eventually the animal may also develop an internal forward model for the effects of its own actions, which would allow for prospective planning of an entire route [32, 46]. An interesting direction for future research is to seek a neuromorphic circuit model for such action planning; perhaps it can be built naturally on top of the endotaxis circuit.
Goal-directed navigation can be based on world-centered (allocentric) or body-centered (egocentric) representations of the environment, mediated by a wide network of interconnected brain regions, including hippocampus, striatum and prefrontal cortex. The relative contribution of these regions to navigation from novel or familiar routes, that demand a different degree of flexibility in the use of the stored spatial representations, has not been completely explored. To address this issue, we trained mice to find a reward relying on allocentric or egocentric information, in a modified version of the cross-maze task. Then we used Zif268 expression to map brain activation when well-trained mice were required to find the goal from a novel or familiar location. Successful navigation was correlated with the activation of CA1, posterior-dorsomedial striatum, nucleus accumbens core and infralimbic cortex when allocentric-trained mice needed to use a novel route. Allocentric navigation from a familiar route activated dorsomedial striatum, nucleus accumbens, prelimbic and infralimbic cortex. None of the structures analyzed was significantly activated in egocentric-trained mice, irrespective of the starting position. These data suggest that a flexible use of stored allocentric information, that allows goal finding even from a location never explored during training, induces a shift from fronto-striatal to hippocampal circuits.
Mapping neuronal ensembles by immunohistochemical visualization of the IEG Zif268, we identified a network of anatomically interconnected brain regions, that becomes functional when mice are required to use allocentric information to solve the cross-maze task. We showed that successful memory retrieval in the allocentric version of the cross maze is correlated with the co-activation of a wide network of brain regions, comprising anterior and posterior DMS, NacC, NacS, PL and IL mPFC. Interestingly, the dorsal CA1 of the hippocampus was selectively activated only when allocentric-trained mice were required to reach the goal from a novel starting position. Hippocampal engagement in the test from the novel position, was accompanied by a more limited recruitment of DMS, Nacc and mPFC compared to testing from a familiar starting position. Indeed, only the pDMS, the NacC and the IL mPFC were significantly activated in the test from novel position. These results were confirmed by an inter-regional correlation analysis, which highlighted a prominent functional connectivity of the hippocampus with the other brain structures analyzed in allocentric mice tested from the novel starting point, and a strong reduction in CA1 connectivity with the mPFC in mice tested from a familiar position. These findings support the idea that a flexible use of spatial representations to reach the goal requires the activation of a different network, as compared to navigation through familiar paths.
The DMS, and in particular the pDMS, has been previously implicated in forms of hippocampal-dependent spatial navigation10,11,14,15,16,17,55,56. We observed not only a functional difference between the pDMS and the aDMS, but also not overlapping roles between DMS subregions and the hippocampus. The pDMS was activated when allocentric-trained mice were confronted with either a novel or a familiar starting position. In contrast, the aDMS was significantly activated only in the familiar condition. These results are coherent with previous data in rats trained in the classical version of the cross-maze, showing a prominent role of the pDMS, but not the aDMS, in using a place strategy during the initial stages of learning and when rats where tested from a novel position17.
An increasing amount of evidence demonstrates that the ventral part of the striatum, the Nacc, is not only an important component of the reinforcement learning system57 but also of the spatial navigation system49,58,59,60,61,62. Cells with spatial-related activity have been described in both the NacS and the NacC58,60. As in the case of the DMS, we found a dissociation between Nacc subregions. The NacC was activated, compared to controls, both in the novel and familiar conditions, while the NacS was selectively activated when mice were tested from a familiar starting position.
Neurons with spatial correlates, especially related to spatial goals, were found both in PL and IL mPFC76 and inactivation of PL-IL caused an impairment in rats trained to shift from a place to a response strategy or viceversa in the cross maze, that was interpreted as a deficit in behavioral flexibility involving cross-modal change of strategy77. In contrast, PL-IL temporary inactivation did not affect reversal learning, a form of intramodal shift that requires learning to respond to a novel goal location77. Our results indicate a differential involvement of mPFC subregions in allocentric navigation, with the IL activated both in the test from the novel and the familiar starting point, and the PL selectively activated in the familiar condition, similarly to the aDMS and the NacS. This result is particularly intriguing in light of increasing evidence showing a dichotomous role of these two mPFC subregions in a variety of processes, including regulation of fear and flexible goal-oriented learning78,79,80. 589ccfa754
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