title: A neural mass model of place cell activity: theta phase precession, replay and imagination of never experienced paths.
abstract: Recent results on hippocampal place cells show that the replay of behavioral sequences does not simply reflect previously experienced trajectories, but may also occur in the reverse direction, or may even include never experienced paths. In order to elucidate the possible mechanisms at the basis of this phenomenon, we have developed a model of sequence learning. The present model consists of two layers of place cell units. Long-range connections among units implement heteroassociation between the two layers, trained with a temporal Hebb rule. The network was trained assuming that a virtual rat moves within a virtual maze. This training leads to the formation of bidirectional synapses between the two layers, i.e. synapses connecting a neuron both with its previous and subsequent element in the path. Subsequently, two distinct conditions were simulated with the trained network. During an exploratory phase, characterized by a similar consideration to the external environment and to the internal representation, the model simulates the occurrence of theta precession in the forward path and the temporal compression. During an imagination phase, when there is no consideration to the external location, the model produces trains of gamma oscillations, without the presence of a theta rhythm, and simulates the occurrence of both direct and reverse replay, and the imagination of never experienced paths. The new paths are built by combining bunches of previous trajectories. The main mechanisms at the basis of this behavior are explained in detail, and lines for future improvements (e.g., to simulate preplay) are discussed.
authors.affiliation: Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi", University of Bologna, Via Venezia 52, 47521, Cesena, Italy, filippo.cona2@unibo.it.
authors.forename: Filippo
authors.initials: F
authors.lastname: Cona
authors.forename: Mauro
authors.initials: M
authors.lastname: Ursino
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D006624:Hippocampus / Q000166:cytology; D007092:Imagination; D018782:Maze Learning / Q000502:physiology; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D051381:Rats; D013028:Space Perception / Q000502:physiology*; D013569:Synapses / Q000502:physiology; D013826:Theta Rhythm / Q000502:physiology*; D014584:User-Computer Interface