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Neuronal morphology generates high-frequency firing resonance.

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Date
2015-05
Authors
Ostojic, Srdjan
Szapiro, Germán
Schwartz, Eric
Barbour, Boris
Brunel, Nicolas
Hakim, Vincent
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Abstract
The attenuation of neuronal voltage responses to high-frequency current inputs by the membrane capacitance is believed to limit single-cell bandwidth. However, neuronal populations subject to stochastic fluctuations can follow inputs beyond this limit. We investigated this apparent paradox theoretically and experimentally using Purkinje cells in the cerebellum, a motor structure that benefits from rapid information transfer. We analyzed the modulation of firing in response to the somatic injection of sinusoidal currents. Computational modeling suggested that, instead of decreasing with frequency, modulation amplitude can increase up to high frequencies because of cellular morphology. Electrophysiological measurements in adult rat slices confirmed this prediction and displayed a marked resonance at 200 Hz. We elucidated the underlying mechanism, showing that the two-compartment morphology of the Purkinje cell, interacting with a simple spiking mechanism and dendritic fluctuations, is sufficient to create high-frequency signal amplification. This mechanism, which we term morphology-induced resonance, is selective for somatic inputs, which in the Purkinje cell are exclusively inhibitory. The resonance sensitizes Purkinje cells in the frequency range of population oscillations observed in vivo.
Type
Journal article
Subject
Cerebellum
Purkinje Cells
Neurons
Animals
Rats
Rats, Wistar
Action Potentials
Male
Permalink
https://hdl.handle.net/10161/23356
Published Version (Please cite this version)
10.1523/jneurosci.3924-14.2015
Publication Info
Ostojic, Srdjan; Szapiro, Germán; Schwartz, Eric; Barbour, Boris; Brunel, Nicolas; & Hakim, Vincent (2015). Neuronal morphology generates high-frequency firing resonance. The Journal of neuroscience : the official journal of the Society for Neuroscience, 35(18). pp. 7056-7068. 10.1523/jneurosci.3924-14.2015. Retrieved from https://hdl.handle.net/10161/23356.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
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Scholars@Duke

Brunel

Nicolas Brunel

Duke School of Medicine Distinguished Professor in Neuroscience
We use theoretical models of brain systems to investigate how they process and learn information from their inputs. Our current work focuses on the mechanisms of learning and memory, from the synapse to the network level, in collaboration with various experimental groups. Using methods fromstatistical physics, we have shown recently that the synapticconnectivity of a network that maximizes storage capacity reproducestwo key experimentally observed features: low connection proba
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