NEURAL-NETWORK MODELING OF THE HIPPOCAMPAL-FORMATION SPATIAL SIGNALS AND THEIR POSSIBLE ROLE IN NAVIGATION - A MODULAR APPROACH

Cells throughout the hippocampal formation show striking spatial firin g correlates as a rat navigates through space. These cells are thought to play a critical role in orchestrating the navigational abilities o f the animals, since damage to the hippocampal formation causes spatia l learning deficits. Here, we present a theoretical framework aimed at explaining how the different spatial signals are generated, as weft a s how they may help guide navigational behavior.Earlier work from our laboratory has presented a simple model for how the location-related s ignals exhibited by hippocampal place cells could be generated, based on convergent sensory information. Here, the results of this work are combined with two more recent models, to provide a more comprehensive theoretical framework. Specifically, we present 1) A neural network mo del of head direction cells, based on the idea that the directional si gnals are generated using a path integration mechanism. Cells which co mbine directional and angular head velocity information project onto t he head direction cells, to ''update'' the current directional signal. This model reproduces the basic phenomenon of direction-specific firi ng, as well as the anticipatory nature of this firing, reported for so me head direction cells. 2) A network simulation of how the hippocampa l spatial signals could be used to orchestrate instrumental learning. Here, place and directional signals converge onto motor cells, each of which are thus driven to fire to specific combinations of location an d directional heading. Each active motor cell generates a small leftwa rd or rightward ''step'' of the simulated animal. When the simulated g oal is encountered, recently active synapses are strengthened, so that goal-directed trajectories are ''stamped in.'' We have found these mo dels useful in helping to clarify our thinking about the proposed theo retical principles, as well as in generating testable predictions. (C) 1997 Wiley-Liss, Inc.