Temporal sequence compression by an integrate-and-fire model of hippocampal area CA3

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 repro ducible sequence of "place cell" firings. With this perspective, spatial na vigation 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 exp eriments by McNaughton and colleagues suggest the hippocampus can recall a sequence of place cell firings at a faster rate than it was experienced. Th is speedup, which occurs during slow-wave sleep, is called temporal compres sion. Here, we show that a simplified model of hippocampal area CA3, based on integrate-and-fire cells and unsupervised Hebbian learning, reproduces t his 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 d ynamics between learning and recall. During learning, the dynamics are pace d by external input and slowed by a low overall level of activity. During r ecall, however, external input is absent, and the dynamics are controlled b y intrinsic network properties. Raising the activity level by lowering inhi bition increases the rate at which the network can transition between previ ously learned states and thereby produces temporal compression. The tendenc y for speeding up future activations, however, is limited by the temporal r ange of associations that were present during learning.