Quantitative prediction of intermittent high-frequency oscillations in neural networks with supralinear dendritic interactions.
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. However, recent single-neuron experiments have demonstrated strongly supralinear dendritic enhancement of synchronous inputs. What are the implications of this amplification for networks of neurons? Here, I show numerically and analytically that such networks can generate intermittent, strong increases of activity with high-frequency oscillations; the models developed predict the shape of these events and the oscillation frequency. As an example, for the hippocampal region CA1, events with 200-Hz oscillations are predicted. I argue that these dynamics provide a plausible explanation for experimentally observed sharp-wave/ripple events. High-frequency oscillations can involve the replay of spike patterns. The models suggest that these patterns may reflect underlying network structures.
Memmesheimer
Raoul-Martin
RM
Faculty of Arts and Sciences, Harvard University, Center for Brain Science, Cambridge, MA 02138, USA. rmmemm@fas.harvard.edu
D000200:Action Potentials / Q000502:physiology; D000818:Animals; D003712:Dendrites / Q000502:physiology; D055724:Electrophysiological Phenomena; D006624:Hippocampus / Q000502:physiology; D006801:Humans; D016014:Linear Models; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D018091:Receptors, AMPA / Q000037:antagonists & inhibitors / Q000502:physiology
D018091:Receptors, AMPA