The place-cell representation of volumetric space in rats

Place cells are spatially modulated neurons found in the hippocampus that underlie spatial memory and navigation: how these neurons represent 3D space is crucial for a full understanding of spatial cognition. We wirelessly recorded place cells in rats as they explored a cubic lattice climbing frame which could be aligned or tilted with respect to gravity. Place cells represented the entire volume of the mazes: their activity tended to be aligned with the maze axes, and when it was more difficult for the animals to move vertically the cells represented space less accurately and less stably. These results demonstrate that even surface-dwelling animals represent 3D space and suggests there is a fundamental relationship between environment structure, gravity, movement and spatial memory

Irregular distribution of grid cell firing fields in rats exploring a 3D volumetric space

We investigated how entorhinal grid cells encode volumetric space. On a horizontal surface, grid cells usually produce multiple, spatially focal, approximately circular firing fields that are evenly sized and spaced to form a regular, close-packed, hexagonal array. This spatial regularity has been suggested to underlie navigational computations. In three dimensions, theoretically the equivalent firing pattern would be a regular, hexagonal close packing of evenly sized spherical fields. In the present study, we report that, in rats foraging in a cubic lattice, grid cells maintained normal temporal firing characteristics and produced spatially stable firing fields. However, although most grid fields were ellipsoid, they were sparser, larger, more variably sized and irregularly arranged, even when only fields abutting the lower surface (equivalent to the floor) were considered. Thus, grid self-organization is shaped by the environment’s structure and/or movement affordances, and grids may not need to be regular to support spatial computations

The role of the ventral hippocampus in probabilistic reversal learning in mice

Recent evidence suggests that during decision making in uncertain dynamic environments humans and mice often rely on hidden state inference to guide their behaviour. Midbrain dopamine dynamics were also proposed to reflect such use of hidden states. We hypothesised that hippocampus might be involved in hidden state inference and that it may provide state-based predictions to the dopaminergic midbrain. However, the exact contribution of hippocampus and the circuit basis of these computations are not fully understood. To investigate this, we used a probabilistic reversal task in mice, a behavioural paradigm in which for optimal performance it is necessary to continuously integrate past trial outcomes to infer the identity of the currently most rewarding choice. Probing mouse behaviour with computational modelling, we found that it was best fit by either value updating with forgetting or state inference models. However, by recording dopamine release in the nucleus accumbens (NAc) during the task, we found that it was most strongly predicted by error associated with the hidden state inference. We also found that inactivation of ventral hippocampus (vH) rendered the dopamine release more aligned with predictions from the value-based strategy, suggesting role of vH in hidden state inference. We subsequently identified that state-based predictions from vH may reach the ventral tegmental area via disynaptic projection pathways though prefrontal cortex (PFC) and NAc. Using in vivo optogenetics we found that NAc-projecting vH neurons were specifically implicated in outcome-based updating, while PFC projection was involved in the outcome-independent action-selection. Overall, in this thesis I explored the strategies and neural basis of flexible decision making in mice. Our findings provide evidence for a role of vH in hidden state inference and the circuit basis for its influence over midbrain dopamine dynamics during probabilistic reversal behaviour

Nucleus accumbens dopamine release reflects Bayesian inference during instrumental learning

Dopamine release in the nucleus accumbens has been hypothesized to signal reward prediction error, the difference between observed and predicted reward, suggesting a biological implementation for reinforcement learning. Rigorous tests of this hypothesis require assumptions about how the brain maps sensory signals to reward predictions, yet this mapping is still poorly understood. In particular, the mapping is non-trivial when sensory signals provide ambiguous information about the hidden state of the environment. Previous work using classical conditioning tasks has suggested that reward predictions are generated conditional on probabilistic beliefs about the hidden state, such that dopamine implicitly reflects these beliefs. Here we test this hypothesis in the context of an instrumental task (a two-armed bandit), where the hidden state switches repeatedly. We measured choice behavior and recorded dLight signals reflecting dopamine release in the nucleus accumbens core. Model comparison based on the behavioral data favored models that used Bayesian updating of probabilistic beliefs. These same models also quantitatively matched the dopamine measurements better than non-Bayesian alternatives. We conclude that probabilistic belief computation plays a fundamental role in instrumental performance and associated mesolimbic dopamine signaling