Competitive Hebbian learning and the hippocampal place cell system: modeling the interaction of visual and path integration cues.
The hippocampus has long been thought essential for implementing a cognitive map of the environment. However, almost 30 years since place cells were found in rodent hippocampal field CA1, it is still unclear how such an allocentric representation arises from an ego-centrically perceived world. By means of a competitive Hebbian learning rule responsible for coding visual and path integration cues, our model is able to explain the diversity of place cell responses observed in a large set of electrophysiological experiments with a single fixed set of parameters. Experiments included changes observed in place fields due to exploration of a new environment, darkness, retrosplenial cortex inactivation, and removal, rotation, and permutation of landmarks. To code for visual cues for each landmark, we defined two perceptual schemas representing landmark bearing and distance information over a linear array of cells. The information conveyed by the perceptual schemas is further processed through a network of adaptive layers which ultimately modulate the resulting activity of our simulated place cells. In path integration terms, our system is able to dynamically remap a bump of activity coding for the displacement of the animal in relation to an environmental anchor. We hypothesize that path integration information is computed in the rodent posterior parietal cortex and conveyed to the hippocampus where, together with visual information, it modulates place cell activity. The resulting network yields a more direct treatment of partial remapping of place fields than other models. In so doing, it makes new predictions regarding the nature of the interaction between visual and path integration cues during new learning and when the system is challenged with environmental changes.
Guazzelli
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University of Southern California Brain Project, Los Angeles, USA. aguazzel@rana.usc.edu
Bota
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Arbib
M A
MA
D000818:Animals; D003198:Computer Simulation; D003624:Darkness; D004779:Environment Design; D006624:Hippocampus / Q000166:cytology / Q000502:physiology*; D018782:Maze Learning / Q000502:physiology*; D008959:Models, Neurological; D009434:Neural Pathways; D009949:Orientation / Q000502:physiology*; D013028:Space Perception / Q000502:physiology*