Natalie Schieferstein: Hippocampal ripple oscillations in inhibitory network models: Analyses on microscopic, mesoscopic, and mean-field scales

BCCN Berlin / GRK 1589 / Humboldt-Universität zu Berlin

Abstract

Brain activity is organized by a variety of rhythms that correlate with cognitive functions. During slow wave sleep or quiet rest, hippocampal activity is characterized by sharp wave-ripples (SPW-Rs): transient (∼50–100 ms) periods of elevated neuronal activity modulated by a fast oscillation — the ripple (∼140–220 Hz). SPW-Rs have been linked to memory consolidation as they co-occur with the replay of behaviorally relevant neuronal activity. Yet, the generation mechanism of ripple oscillations remains unclear. Multiple potential mechanisms have been proposed, relying on excitation and/or inhibition as the main pacemaker. A prominent model for ripple generation is based on delayed inhibitory feedback in a population of interneurons. Recent support for this model has come from the observation that it can reproduce the experimentally observed intra-ripple frequency accommodation (IFA) — a decrease in the instantaneous ripple frequency over the course of a ripple event triggered by transient, sharp wave-like stimulation. A mechanistic understanding of IFA could thus advance model selection. This thesis analyses ripple oscillations in inhibitory network models at micro-, meso-, and macroscopic scales and elucidates how the ripple dynamics and IFA depend on the excitatory drive, inhibitory coupling strength, and the noise model.

 

Additional Information

PhD defense in the research training group GRK 1589, 'Sensory Computation in Neural Systems'.

Organized by

Prof. Dr. Richard Kempter/Lisa Velenosi