TY - JOUR T1 - Functional connectivity models for brain state identification: application to decoding of spatial representations from hippocampal CA1 and CA3 recordings JF - bioRxiv DO - 10.1101/073759 SP - 073759 AU - L. Posani AU - S. Cocco AU - K. Jezek AU - R. Monasson Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/09/20/073759.abstract N2 - 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. 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 efficiently decodes maps in CA3 and outperfoms existing methods in CA1, where maps are much less orthogonal. 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 for on much longer (> 1 min) intervals, both in CA3 and in CA1. In addition, we introduce an efficient Bayesian decoder of the rat full trajectory over time, and find that the animal location can be accurately predicted at all times, even during flickering events. Precise information about the animal position is thus always present in the neural activity, irrespectively of the dynamical shifts in the recalled maps.Significance Statement Hippocampus can store spatial representations (maps), which are retrieved everytime an animal is placed in the corresponding environment. Decoding maps from the instantaneous activity of recorded cells is crucial to track the fast hippocampal dynamics e.g. in response to changes in sensory inputs, but is difficult, especially in CA1 where maps are much less orthogonal than in CA3. We show that functional-connectivity based decoders, which exploit only the pairwise correlations in the recorded cell activities, are accurate both in CA3 and CA1. Our decoder allows us to investigate map retrieval in experiments, where environmental cues are suddenly switched, and could be, more generally, applied to identify brain states in other areas even in the absence of sensory correlates. ER -