  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 metric
representation of space. Currently, any of the existing theoretical models can
explain all aspects of GC-specific spatially selective patterns of activity. It
is 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 to
extract high principal components from the activity of the place cells could
provide the basis for GC-like activity patterns development. Supporting this
hypothesis is the finding that PC activity remains spatially stable after the
disruption of the GC firing patterns and that grid patterns almost disappear
when hippocampal cells are deactivated. Development of the early PCs before GCs
formation also supports the role of PCs as the spatial information providers to
GCs. Here we have developed a new theoretical model of grid fields formation
based on synaptic plasticity in synapses connecting PCs in hippocampus and
neurons in entorhinal cortex. Learning of hexagonally symmetric fields in the
model is due to complex action of several simple biologicaly motivated synaptic
plasticity rules. These rules include associative synaptic plasticity rules
similar to BCM rule and homeostatic plasticity rules which restrict synaptic
weigths. In contrast to previously described models, this network could learn
GC patterns after a very short experience of navigation in a novel environment.
In conclusion we suggest that learning on the basis of simple and biologically
plausible associative synaptic plasticity rules could contribute to the
formation of grid fields in early development and to maintainence of normal GCs
activity patterns in familiar environments.
