title: Temporal sequence compression by an integrate-and-fire model of hippocampal area CA3.
abstract: Cells in the rat hippocampus fire as a function of the animal's location in space. Thus, a rat moving through the world produces a statistically reproducible sequence of "place cell" firings. With this perspective, spatial navigation can be viewed as a sequence learning problem for the hippocampus. That is, learning entails associating the relationships among a sequence of places that are represented by a sequence of place cell firing. Recent experiments by McNaughton and colleagues suggest the hippocampus can recall a sequence of place cell firings at a faster rate than it was experienced. This speedup, which occurs during slow-wave sleep, is called temporal compression. Here, we show that a simplified model of hippocampal area CA3, based on integrate-and-fire cells and unsupervised Hebbian learning, reproduces this temporal compression. The amount of compression is proportional to the activity level during recall and to the relative timespan of associativity during learning. Compression seems to arise from an alteration of network dynamics between learning and recall. During learning, the dynamics are paced by external input and slowed by a low overall level of activity. During recall, however, external input is absent, and the dynamics are controlled by intrinsic network properties. Raising the activity level by lowering inhibition increases the rate at which the network can transition between previously learned states and thereby produces temporal compression. The tendency for speeding up future activations, however, is limited by the temporal range of associations that were present during learning.
authors: University of Virginia Health Sciences Center, Charlottesville 22908, USA. august@virginia.edu
authors: D A
authors: DA
authors: August
authors: W B
authors: WB
authors: Levy
mesh_terms: D000818:Animals; D003463:Cues; D006624:Hippocampus / Q000166:cytology / Q000502:physiology*; D008564:Membrane Potentials / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D051381:Rats; D013569:Synapses / Q000502:physiology; D013997:Time Factors