A non-spatial account of place and grid cells based on clustering models of concept learning

One view is that conceptual knowledge is organized using the circuitry in the medial temporal lobe (MTL) that supports spatial processing and navigation. In contrast, we find that a domain-general learning algorithm explains key findings in both spatial and conceptual domains. When the clustering model is applied to spatial navigation tasks, so-called place and grid cell-like representations emerge because of the relatively uniform distribution of possible inputs in these tasks. The same mechanism applied to conceptual tasks, where the overall space can be higher-dimensional and sampling sparser, leading to representations more aligned with human conceptual knowledge. Although the types of memory supported by the MTL are superficially dissimilar, the information processing steps appear shared. Our account suggests that the MTL uses a general-purpose algorithm to learn and organize context-relevant information in a useful format, rather than relying on navigation-specific neural circuitry

Hippocampal predictive maps of an uncertain world

Humans and other animals can solve a wide variety of decision-making problems with remarkable flexibility. This flexibility is thought to derive from an internal model of the world, or ‘cognitive map’, used to predict the future and plan actions accordingly. A recent theoretical proposal suggests that the hippocampus houses a representation of long-run state expectancies. These “successor representations” (SRs) occupy a middle ground between model-free and model-based reinforcement learning strategies. However, it is not clear whether SRs can explain hippocampal contributions to spatial and model-based behaviour, nor how a putative hippocampal SR might interface with striatal learning mechanisms. More generally, it is not clear how the predictive map should encode uncertainty, and how an uncertainty-augmented predictive map modifies our experimental predictions for animal behaviour. In the first part of this thesis, I investigated whether viewing the hippocampus as an SR can explain experiments contrasting hippocampal and dorsolateral striatal contributions to behaviour in spatial and non-spatial tasks. To do this, I modelled the hippocampus as an SR and DLS as model-free reinforcement learning, combining their outputs via their relative reliability as a proxy for uncertainty. Current SR models do not formally address uncertainty. Therefore I extended the learning of SRs by temporal differences to include managing uncertainty in new observations versus existing knowledge. I generalise this approach to a multi-task setting using a Bayesian nonparametric switching Kalman Filter, allowing the model to learn and maintain multiple task-specific SR maps and infer which one to use at any moment based on the observations. I show that this Bayesian SR model captures animal behaviour in tasks which require contextual memory and generalisation. In conclusion, I consider how the hippocampal contribution to behaviour can be considered as a predictive map when adapted to take account of uncertainty and combined with other behavioural controllers

Prediction and memory: A predictive coding account

The hippocampus is crucial for episodic memory, but it is also involved in online prediction. Evidence suggests that a unitary hippocampal code underlies both episodic memory and predictive processing, yet within a predictive coding framework the hippocampal-neocortical interactions that accompany these two phenomena are distinct and opposing. Namely, during episodic recall, the hippocampus is thought to exert an excitatory influence on the neocortex, to reinstate activity patterns across cortical circuits. This contrasts with empirical and theoretical work on predictive processing, where descending predictions suppress prediction errors to ‘explain away’ ascending inputs via cortical inhibition. In this hypothesis piece, we attempt to dissolve this previously overlooked dialectic. We consider how the hippocampus may facilitate both prediction and memory, respectively, by inhibiting neocortical prediction errors or increasing their gain. We propose that these distinct processing modes depend upon the neuromodulatory gain (or precision) ascribed to prediction error units. Within this framework, memory recall is cast as arising from fictive prediction errors that furnish training signals to optimise generative models of the world, in the absence of sensory data