DCN Lecture by dr. Raoul-Martin Memmesheimer (Lezing)

Title:Quantitative prediction of intermittent high-frequency oscillations in neural networks with nonlinear dendritic interactions

Abstract
The explanation of higher neural processes requires an understanding of the dynamics of complex spiking neural networks. So far, modeling studies have focused on networks with linear or sublinear dendritic input summation only. However, recent single neuron experiments have demonstrated strongly nonlinear dendritic enhancement of synchronous inputs. What are the implications of this amplification for networks of neurons? We show numerically and analytically that such networks generate intermittent, strong increases of activity with high-frequency oscillations; the models predict the event shape and the oscillation frequency. As an example, for the hippocampal region CA1, events with 200Hz oscillations are predicted. We argue that this dynamics provides a plausible explanation for experimentally observed sharp-wave/ripple events. Sharp wave/ripples are considered to be important for memory and lead to the replay of spike patterns from preceding learning phases. The phenomenon is covered by our models in a simple manner as the oscillatory spiking activity reflects underlying network structures.