The hippocampus is a critical structure for learning and memory in the mammalian brain. During active exploration, hippocampal circuits support the activity of place cells that track an animal?s position as it moves. The spatial representations of place cells in a particular environment collectively form a spatial map, the function of which remains largely unclear. During periods of rest, the hippocampus switches to a network state characterized by stochastic and highly variable activity patterns. A prominent feature of this irregular activity is recurring strong bursts of excitation, called sharp waves, that recruit large proportions of hippocampal neurons and sweep across the circuits of the hippocampus. Sequentially ordered activation of place cells during sharp waves is considered to serve a critical role in memory consolidation. In addition to replaying recently experienced routes, sharp wave sequences can follow unexplored paths through space and can be biased to goal destinations according to reward value. This project will investigate an explicit theoretical framework for flexible sequence generation in service of prospective route planning for navigation. The theory rests on the observation that the same network that initiates sharp waves, the hippocampal CA3 region, also prominently carries a distinct lower-frequency band of gamma oscillation. One study has shown that the slow gamma rhythm modulated spiking activity during sharp wave sequences, such that the phase of gamma with the strongest activity corresponded to periods when the decoded sequence would dwell, or ?hover?, at discrete spatial locations. Informed by recording data from that study, this project will develop realistic hippocampal network models to study how sharp waves and slow gamma oscillations might emerge simultaneously. On the basis of that physiological model, spatial activity will be embedded into the synaptic weights of the network to assess whether sharp waves and slow gamma interact to support gamma-locked attractor dynamics, and how that interaction depends quantitatively on the synaptic modifications for spatial learning. In particular, focus will be placed on a puzzling experimental demonstration that sharp wave sequence recall slows down during learning. The long-term objective is to fully evaluate whether these emergent phenomena support a unified framework for the function of spatial maps in navigation on the basis of enabling flexible generation of novel sequences. A clear theoretical understanding of these key hippocampal phenomena in realistic networks will elucidate their role in memory, planning, and disease states.
How do the neuronal networks of the hippocampus learn to generate sequences of spatial activity that help animals find their way in a changing world? We propose computational models and mathematical analysis to investigate a theory in which the tendency of recurrent networks to converge to a small number of low-energy states is rhythmically destabilized by gamma oscillations concurrently generated by the network. Our project integrates theory with benchmarks from high-density recording data to reveal neurocomputational functions of hippocampal activity patterns that serve critical roles in health and disease.
