We noted that during learning, there were often multiple detected

We noted that during learning, there were often multiple detected SWRs per trial (Figure 5B), indicating that reactivation events could contribute to subsequent choices in multiple ways. If, for example, there is reactivation of both possible upcoming trajectories (the correct see more future and incorrect future trajectory), then reactivation events could serve to provide information about possible upcoming choices to other brain regions that would then evaluate those possibilities and make a decision. Alternatively, if there is only reactivation of the correct future trajectory, then reactivation events could inform downstream brain regions

of the correct future path. Finally, if only reactivation of the most recent past trajectory occurs, then reactivation events could provide information about a specific past experience. This would inform downstream areas of the specific past experience necessary for the subsequent decision about which outer arm to visit next. The place cells we recorded were generally active in both directions of motion (Karlsson and Frank, 2009), consistent with previous observations

for place cells in novel environments (Frank et al., ABT-263 order 2004). As a result, we cannot unambiguously separate forward from reverse replay events in this data set. Further, it is not yet clear how downstream brain areas interpret forward and reverse replay. We therefore classified events using only the direction of propagation of the spatial representation. In particular, we asked whether SWR reactivation

events preceding correct trials were more likely to reflect outbound paths that progressed away from the animal or to reflect inbound paths that progressed toward the animal (Figure 6A, Figure S2A). We focused on the reactivation events present during task acquisition (performance categories Oxalosuccinic acid 2 and 3), although the results were similar across all performance categories (Figures S2A and S2B). For these analyses, we used a previously developed decoding algorithm (Davidson et al., 2009; Karlsson and Frank, 2009) that translates neural activity during SWRs into trajectories through the environment. These trajectories consist of a probability distribution function (pdf) over location for a series of 15 ms bins in which there is spiking during the SWR. We fit a line to samples from the sequence of pdfs and assigned each SWR as either outbound or inbound based on the progression of spatial representations within the SWR. Increases in distance with time manifest as a positive slope of the line, consistent with outbound trajectories from the center arm to an outside arm. We have previously shown that most replay events begin with locations near the animal and proceed to more distant locations (Karlsson and Frank, 2009).

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