GENERAL RESEARCH INTERESTS

How are self-motion cues integrated to influence the activity of grid cells in Entorhinal Cortex?

The entorhinal cortex (EC) is rich in cells with specific activity patterns. Perhaps the best-known of these are the "grid cells" native primarily to layer II of the entorhinal cortex, but there are several other distinct types of cells carrying other information. Two of these signals are the "head direction" and "speed" cells, that code directional facing and running speed, respectively. Together, these two cell types are sufficient to produce a robust velocity signal. Leading models of grid cell formation postulate that a velocity signal is necessary to produce their regular firing patterns. My research attempts to elucidate this link using single-unit in vivo recordings of grid, head direction and speed cells in EC together with environmental and genetic perturbations known to modify grid cell activity.

How are long timescales represented by the hippocampus? How might this information be useful elsewhere?

A single episodic memory is represented by a tripartite code: What happened, Where it happened and When it happened. Decades of research has ably shown that the hippocampus is a structure that is critical to the formation of episodic and spatial memory. The discovery of "place cells"; cells that fire only in a specific part of an environment, gave us the substrate to how the hippocampus might code "where". Subsequent research has shown that these cells are extremely flexible while remaining spatially specific - they will adjust their firing rates and positional preference based on changes in the environment, or the placement of objects within that environment. These characteristics have shown us how the hippocampus might code "what".

How exactly the hippocampus codes "when" has been rather more elusive. Recent research has demonstrated that these cells can keep track of short timescales on the order of seconds, but it is clear that memory is represented on a much longer timescale than this. My research used extremely long recordings (24-52 hours) following the same neurons over several day-long exposures to the same environment with no external time cues. I have found that place cells have a robust sinusoidal modulation in their firing rates, the period of which closely matches a circadian cycle. EEG recordings and forced-stimulation of theta EEG activity over these timescales showed that hippocampal activity as a whole is able to fluctuate on these timescales. Finally, examination of hippocampal EEG in conjunction with cortical regions of interest showed brief bursts of spectral coherence between these regions at times that closely matched a solar day at frequency bands thought to be relevant for the transfer of information. Together, these studies demonstrate that the hippocampus is capable of representing long timescales, and offers a mechanism through which this might occur.

What is the link between eating, metabolic feedback and emotional state?

The hippocampus is a key structure in the regulation of anxiety; all drugs that are anxiolytic (irrespective of pharmacologic mechanism) reduce the frequency of theta rhythm within hippocampus, while no drug that is not anxiolytic does so. Patients that are chronically obese have much higher rates of depression than the general population, but they also have much higher incidences of anxiety. One reason this might be the case is that hormonal feedback from the ingestion of food may be anxiolytic. Indeed, diabetic patients show the same constellation of comorbidities as obese patients. Both of these syndromes (obesity and diabetes) represent desensitisation to a food-linked hormonal feedback mechanism; namely Leptin and Insulin respectively. The hippocampus is rich with receptors for both Leptin and Insulin, suggesting a central role for this structure in the cognitive and emotional results of evaluating the effects of eating. There is emerging evidence that leptin itself may function as an effective behavioral anxiolytic. I am interested in the way that these signals are processed within hippocampus both in the presence and absence of metabolic disorders, in order to better understand the root causes of obesity and engage with future treatments.