title: A learning rule for place fields in a cortical model: theta phase precession as a network effect.
abstract: We show that a model of the hippocampus introduced recently by Scarpetta et al. (2002, Neural Computation 14(10):2371-2396) explains the theta phase precession phenomena. In our model, the theta phase precession comes out as a consequence of the associative-memory-like network dynamics, i.e., the network's ability to imprint and recall oscillatory patterns, coded both by phases and amplitudes of oscillation. The learning rule used to imprint the oscillatory states is a natural generalization of that used for static patterns in the Hopfield model, and is based on the spike-time-dependent synaptic plasticity, experimentally observed.In agreement with experimental findings, the place cells' activity appears at consistently earlier phases of subsequent cycles of the ongoing theta rhythm during a pass through the place field, while the oscillation amplitude of the place cells' firing rate increases as the animal approaches the center of the place field and decreases as the animal leaves the center. The total phase precession of the place cell is lower than 360 degrees , in agreement with experiments. As the animal enters a receptive field, the place cells' activity comes slightly less than 180 degrees after the phase of maximal pyramidal cell population activity, in agreement with the findings of Skaggs et al. (1996, Hippocampus 6:149-172). Our model predicts that the theta phase is much better correlated with location than with time spent in the receptive field. Finally, in agreement with the recent experimental findings of Zugaro et al. (2005, Nature Neuroscience 9(1):67-71), our model predicts that theta phase precession persists after transient intrahippocampal perturbation.
authors.affiliation: Department of Physics ER Caianiello, University of Salerno, Baronissi, SA, Italy. silvia@sa.infn.it
authors.forename: Silvia
authors.initials: S
authors.lastname: Scarpetta
authors.forename: Maria
authors.initials: M
authors.lastname: Marinaro
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001683:Biological Clocks / Q000502:physiology; D002540:Cerebral Cortex / Q000502:physiology*; D005106:Exploratory Behavior / Q000502:physiology; D006624:Hippocampus / Q000502:physiology; D007858:Learning / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology; D009474:Neurons / Q000502:physiology; D009949:Orientation / Q000502:physiology; D017966:Pyramidal Cells / Q000502:physiology; D051381:Rats; D013028:Space Perception / Q000502:physiology; D009435:Synaptic Transmission / Q000502:physiology; D013826:Theta Rhythm; D013997:Time Factors