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Abstract:
In volatile environments, humans and animals face different forms of uncertainty to which they must adapt to thrive. However, our understanding of the neural basis of this adaptation is incomplete, despite, for instance, long-standing arguments about its possible dependence on neuromodulation. Here, we take advantage of the well-known spatial remapping of hippocampal place cells in the face of environmental change to interrogate these processes. We performed calcium imaging in CA1 place cells in the Uncertain Reward virtual reality Task (UR- Task) [1]. In this, mice run along a linear track and lick for a water reward whose precise location on any run may be more or less certain in a block (a form of expected uncertainty), and which might also translate without warning (unexpected uncertainty). From limpid changes in reward locations, the place map undergoes remapping that is zone-dependent, affecting most the cells between reward zones, and visible as early as within the first trial after the switch. Given inherently variable reward locations, the place map within the reward zone fractionates, with some cells coding distance from reward, other cells firing reliably in relationship to space, and a third group being co-modulated, with reward and space-related fields. Single cell analysis highlights that, while some cells are uniquely modulated by reward or position, there is a rich interplay between position and reward contributions to the place code. We design generalized linear mixed models that can quantify the unique contribution of position and reward (both predictive and responsive) on each individual cell activity and help cluster the place map. We find more co-modulation between reward and position of the cells under expected than unexpected uncertainty, in which cells seem to cluster as only reward- or position-based.
