@article{discovery10056563,
           title = {A neural-level model of spatial memory and imagery},
         journal = {eLife},
          number = {e33752},
           month = {September},
          volume = {7},
            year = {2018},
            note = {This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.},
        keywords = {computational model, episodic memory, neuroscience, none, scene construction, spatial cognition, spatially selective cells, trace cells},
            issn = {2050-084X},
             url = {https://doi.org/10.7554/eLife.33752},
          author = {Bicanski, A and Burgess, N},
        abstract = {We present a model of how neural representations of egocentric spatial experiences in parietal cortex interface with viewpoint-independent representations in medial temporal areas, via retrosplenial cortex, to enable many key aspects of spatial cognition. This account shows how previously reported neural responses (place, head-direction and grid cells, allocentric boundary- and object-vector cells, gain-field neurons) can map onto higher cognitive function in a modular way, and predicts new cell types (egocentric and head-direction-modulated boundary- and object-vector cells). The model predicts how these neural populations should interact across multiple brain regions to support spatial memory, scene construction, novelty-detection, 'trace cells', and mental navigation. Simulated behavior and firing rate maps are compared to experimental data, for example showing how object-vector cells allow items to be remembered within a contextual representation based on environmental boundaries, and how grid cells could update the viewpoint in imagery during planning and short-cutting by driving sequential place cell activity.}
}