Hippocampal theta sequences: from phenomenology to circuit mechanisms
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The hippocampus is a brain structure involved in episodic memory and spatial cognition. Neuronal activity within the hippocampus exhibits intricate temporal patterning, including oscillatory and sequential dynamics, which are believed to underlie these cognitive processes. In individual cells, a temporal activity pattern called phase precession occurs which leads to the organisation of neuronal populations into sequences. These sequences are hypothesised to form a substrate for episodic memory and the representation of spatial trajectories during navigation. In this thesis, I present a novel theory of the phenomenological properties of these neuronal activity sequences. In particular, I propose that the sequential organisation of population activity is governed by the independent phase precession of each cell. By comparison of models in which cells are independent and models in which cells exhibit coordinated activity against experimental data, I provide empirical evidence to support this hypothesis. Further, I show how independent coding affords a vast capacity for the generation of sequential activity patterns across distinct environments, allowing the representation of episodes and spatial experiences across a large number of contexts. This theory is then extended to account for grid cells, whose activity patterns form a hexagonal lattice over external space. By analysing simple forms of phase coding in populations of grid cells, I show how previously undocumented constraints on phase coding in two dimensional environments are imposed by the symmetries of grid cell firing fields. To overcome these constraints, I propose a more complex phenomenological model which can account for phase precession in both place cells and grid cells in two dimensional environments. Using insights from this theory, I then propose a biophysical circuit mechanism for hippocampal sequences. I show that this biophysical circuit model can account for the proposed phenomenological coding properties and provide experimentally testable predictions which can distinguish this model from existing models of phase precession. Finally, I outline a scheme by which this biophysical mechanism can implement supervised learning using spike time dependent plasticity in order to learn associations between events occurring on behavioural timescales. The models presented in this thesis challenge previous theories of hippocampal circuit function and suggest a much higher degree of flexibility and capacity for the generation of sequences than previously believed. This flexibility may underlie our ability to represent spatial experiences and store episodic memories across a seemingly unlimited number of distinct contexts.