|dc.description.abstract||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.||en