Neural correlates of navigation in large-scale space

Navigation and self-localisation are fundamental to spatial cognition. The cognitive map supporting these abilities is implemented in the hippocampal formation. Place cells in the hippocampus fire when the animal is at a specific location – a place field. They are thought to be involved in navigation and self-localisation but usually studied in constrained environments, limiting observable states. In this thesis, I present two experiments studying place cells in large open field environments, a novel auditory cue-triggered navigational task, and a technical solution for conducting large scale automated experiments. Place cells are frequently reactivated during immobility, rapidly replaying trajectories through environments. These replay events are thought to be involved in navigational planning. Using a novel automated cue-triggered navigational task in a large open field environment, I show that replay is not associated with navigation to the goal. Instead, it occurs reliably at the end of successful trials, when an associated reward is received, but not during consumption of scattered pellets. The trajectories in these events are predictive of the animal’s movement after, but not before, the reward. The number of place fields per cell, their size and other properties have not been fully characterised. Using multiple large open field environments of different size, I show that place field size, shape and density changes systematically with distance from walls. However, through a homeostatic mechanism, the mean firing rate and proportion of co-active units in the population remains constant throughout environments, as does the accuracy of their spatial representation. Multiple place field properties are conserved by cells across environments, including the number of fields, which is quantified relative to environment size using a gamma-Poisson model. Place cell population models suggest two sub-populations, with uniform and boundary dependent field distributions. These results provide a comprehensive account of place cell population statistics in different size environments

State transitions in the statistically stable place cell population correspond to rate of perceptual change

The hippocampus occupies a central role in mammalian navigation and memory. Yet an understanding of the rules that govern the statistics and granularity of the spatial code, as well as its interactions with perceptual stimuli, is lacking. We analyzed CA1 place cell activity recorded while rats foraged in different large-scale environments. We found that place cell activity was subject to an unexpected but precise homeostasis-the distribution of activity in the population as a whole being constant at all locations within and between environments. Using a virtual reconstruction of the largest environment, we showed that the rate of transition through this statistically stable population matches the rate of change in the animals’ visual scene. Thus, place fields near boundaries were small but numerous, while in the environment’s interior, they were larger but more dispersed. These results indicate that hippocampal spatial activity is governed by a small number of simple laws and, in particular, suggest the presence of an information-theoretic bound imposed by perception on the fidelity of the spatial memory system

Interpreting wide-band neural activity using convolutional neural networks

Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires considerable knowledge about the nature of the representation and often depends on manual operations. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning framework able to decode sensory and behavioral variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviors, brain regions, and recording techniques. Once trained, it can be analyzed to determine elements of the neural code that are informative about a given variable. We validated this approach using electrophysiological and calcium-imaging data from rodent auditory cortex and hippocampus as well as human electrocorticography (ECoG) data. We show successful decoding of finger movement, auditory stimuli, and spatial behaviors – including a novel representation of head direction - from raw neural activity