@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.} }