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The grid cells (GCs) of the medial entorhinal cortex (MEC) and place cells(PCs) of the hippocampus are key elements of the brain network for the metricrepresentation of space. Currently, any of the existing theoretical models canexplain all aspects of GC-specific spatially selective patterns of activity. Itis also not clear how these patterns are formed during the network development.On the other hand, it was previously shown that the network that can learn toextract high principal components from the activity of the place cells couldprovide the basis for GC-like activity patterns development. Supporting thishypothesis is the finding that PC activity remains spatially stable after thedisruption of the GC firing patterns and that grid patterns almost disappearwhen hippocampal cells are deactivated. Development of the early PCs before GCsformation also supports the role of PCs as the spatial information providers toGCs. Here we have developed a new theoretical model of grid fields formationbased on synaptic plasticity in synapses connecting PCs in hippocampus andneurons in entorhinal cortex. Learning of hexagonally symmetric fields in themodel is due to complex action of several simple biologicaly motivated synapticplasticity rules. These rules include associative synaptic plasticity rulessimilar to BCM rule and homeostatic plasticity rules which restrict synapticweigths. In contrast to previously described models, this network could learnGC patterns after a very short experience of navigation in a novel environment.In conclusion we suggest that learning on the basis of simple and biologicallyplausible associative synaptic plasticity rules could contribute to theformation of grid fields in early development and to maintainence of normal GCsactivity patterns in familiar environments.

Self-organization of grid fields under supervision of place cells in the model of neuron with associative plasticity