Neuronal morphology generates high-frequency firing resonance.
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.
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Journal articlePermalink
https://hdl.handle.net/10161/23356Published Version (Please cite this version)
10.1523/jneurosci.3924-14.2015Publication 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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Show full item recordScholars@Duke
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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