Selected Publications

Here we show that the population dynamics of head direction lie on a neural manifold that includes an informative second radial dimension.  Variability along this dimension is correlated with the overall activity of head direction cells (the 'network gain'), is most pronounced during darkness, and bears signatures of previous experience with informative reference frames - be they static or dynamic. 

Here we show that although the CA1 spatial code for even the same environment changes over the course of weeks, it does so in a very particular way - one which preserve the relative representational structure of context.

Here we show that the firing rate of CA1 place cells is modulated by the recent history of the navigator, even in the absence of disambiguating sensory cues and explicit task demands. Moreover, we show that this firing rate modulation is causally mediated by the trisynaptic pathway (EC->DG->CA3->CA3).

Previous work has shown that the firing fields of place and grid cells are distorted in deformed environments, sparking a lot of debate about the representational content of these codes. Here we show that when a familiar environment is reshaped, the grid pattern undergoes dynamic shifts dependent on the recent history of the navigator, challenging previous interpretations of these data.

As a compliment to the work described above, we noted that the dynamic history-dependent shifts we observed in rodent grid cell patterns motivated an additional prediction - that spatial memory should also show such dynamic history-dependent shifts. Here, we demonstrated these predicted shifts in three separate experiments in humans, including when navigating in immersive VR. 

Previous work has shown that numerous organisms, from insects to toddlers, preferentially rely on the shape of the space around them when reorienting after becoming lost, even when other cues in the world are more informative. Here we show that this provocative behavior is reflected in the hippocampal maps of mice, which also preferentially reorient based on the shape of the surrounding space and predict the navigator's memory-driven behavior.