Abstract
Hippocampus can store spatial representations, or maps, which are recalled each time a subject is placed in the corresponding environment. We consider the problem of decoding the recalled maps as a function of time from multi-cellular recordings in CA1. Maps corresponding to different environments in CA1 mostly differ by changes in firing rates rather than firing fields, and are harder to identify than in CA3, in which maps are essentially orthogonal. We introduce a functional-connectivity-based decoder, which accounts for the pairwise correlations between the spiking activities of neurons in each map and does not require any positional information, i.e. any knowledge about place fields. We first show, on recordings of hippocampal activity in constant environmental conditions, that our decoder outperforms existing decoding methods in CA1. Our decoder is then applied to data from teleportation experiments, in which instantaneous switches between environmental conditions trigger the recall of the corresponding maps. We test the sensitivity of our approach on the transition dynamics between the respective memory states (maps). We find that the rate of spontaneous state shifts (flickering) after a teleportation event is increased not only within the first few seconds as already reported, but the network also shows a higher instability level on much longer (> 1 min) intervals.