During navigation, animals estimate their position using path integration and landmarks. In a series of two studies, we used virtual reality and electrophysiology to dissect how these inputs combine to generate the brain’s spatial representations. In the first study (Campbell et al., 2018), we focused on the medial entorhinal cortex (MEC) and its set of navigationally-relevant cell types, including grid cells, border cells, and speed cells. We discovered that attractor dynamics could explain an array of initially puzzling MEC responses to virtual reality manipulations. This theoretical framework successfully predicted both MEC grid cell responses to additional virtual reality manipulations, as well as mouse behavior in a virtual path integration task. In the second study (Campbell*, Attinger* et al., 2021), we asked whether these principles generalize to other navigationally-relevant brain regions. We used Neuropixels probes to record thousands of neurons from MEC, primary visual cortex (V1), and retrosplenial cortex (RSC). In contrast to the prevailing view that “everything is everywhere all at once,” we identified a unique population of MEC neurons, overlapping with grid cells, that became active with striking spatial periodicity while head-fixed mice ran on a treadmill in darkness. These neurons exhibited unique cue-integration properties compared to other MEC, V1, or RSC neurons: they remapped more readily in response to conflicts between path integration and landmarks; they coded position prospectively as opposed to retrospectively; they upweighted path integration relative to landmarks in conditions of low visual contrast; and as a population, they exhibited a lower-dimensional activity structure. Based on these results, our current view is that MEC attractor dynamics play a privileged role in resolving conflicts between path integration and landmarks during navigation. Future work should include carefully designed causal manipulations to rigorously test this idea, and expand the theoretical framework to incorporate notions of uncertainty and optimality.

Goal-directed navigation requires precise estimates of spatial relationships between current position and future goal, as well as planning of an associated route or action. While neurons in the hippocampal formation can represent the animal’s position and nearby trajectories, their role in determining the animal’s destination or action has been questioned. We thus hypothesize that brain regions outside the hippocampal formation may play complementary roles in navigation, particularly for guiding goal-directed behaviours based on the brain’s internal cognitive map. In this seminar, I will first describe a subpopulation of neurons in the retrosplenial cortex (RSC) that increase their firing when the animal approaches environmental boundaries, such as walls or edges. This boundary coding is independent of direct visual or tactile sensation but instead depends on inputs from the medial entorhinal cortex (MEC) that contains spatial tuning cells, such as grid cells or border cells. However, unlike MEC border cells, we found that RSC border cells encode environmental boundaries in a self-centred egocentric coordinate frame, which may allow an animal for efficient avoidance from approaching walls or edges during navigation. I will then discuss whether the brain can possess a precise estimate of remote target location during active environmental exploration. Such a spatial code has not been described in the hippocampal formation. However, we found that neurons in the rat orbitofrontal cortex (OFC) form spatial representations that persistently point to the animal’s subsequent goal destination throughout navigation. This destination coding emerges before navigation onset without direct sensory access to a distal goal, and are maintained via destination-specific neural ensemble dynamics. These findings together suggest key roles for extra-hippocampal regions in spatial navigation, enabling animals to choose appropriate actions toward a desired destination by avoiding possible dangers.

While various forms of cells have been found in relation to the hippocampus cognitive map and navigation system, how these cells are formed and what is read from them is still a mystery. In the current lecture I will talk about several projects which tackle these issues. First, I will show how the formation of border cells in the coginitive map is related to a coordinate transformation, second I will discuss the interaction between the reward system (VTA) and the hippocampus. Finally I will describe a project using place cells as a proxy for associative memory for assessing deficits in Alzheimer’s disease.

When animals navigate on a two-dimensional (2D) surface, many neurons in the medial entorhinal cortex (MEC) are activated as the animal passes through multiple locations (‘firing fields’) arranged in a hexagonal lattice that tiles the locomotion-surface; these neurons are known as grid cells. However, although our world is three-dimensional (3D), the 3D volumetric representation in MEC remains unknown. Here we recorded MEC cells in freely-flying bats and found several classes of spatial neurons, including 3D border cells, 3D head-direction cells, and neurons with multiple 3D firing-fields. Many of these multifield neurons were 3D grid cells, whose neighboring fields were separated by a characteristic distance – forming a local order – but these cells lacked any global lattice arrangement of their fields. Thus, while 2D grid cells form a global lattice – characterized by both local and global order – 3D grid cells exhibited only local order, thus creating a locally ordered metric for space. We modeled grid cells as emerging from pairwise interactions between fields, which yielded a hexagonal lattice in 2D and local order in 3D – thus describing both 2D and 3D grid cells using one unifying model. Together, these data and model illuminate the fundamental differences and similarities between neural codes for 3D and 2D space in the mammalian brain.