Literature DB >> 31286211

Frequency-dependent responses of neuronal models to oscillatory inputs in current versus voltage clamp.

Horacio G Rotstein1,2,3,4, Farzan Nadim5,6.   

Abstract

Action potential generation in neurons depends on a membrane potential threshold and therefore on how subthreshold inputs influence this voltage. In oscillatory networks, for example, many neuron types have been shown to produce membrane potential ([Formula: see text]) resonance: a maximum subthreshold response to oscillatory inputs at a nonzero frequency. Resonance is usually measured by recording [Formula: see text] in response to a sinusoidal current ([Formula: see text]), applied at different frequencies (f), an experimental setting known as current clamp (I-clamp). Several recent studies, however, use the voltage clamp (V-clamp) method to control [Formula: see text] with a sinusoidal input at different frequencies [[Formula: see text]] and measure the total membrane current ([Formula: see text]). The two methods obey systems of differential equations of different dimensionality, and while I-clamp provides a measure of electrical impedance [[Formula: see text]], V-clamp measures admittance [[Formula: see text]]. We analyze the relationship between these two measurement techniques. We show that, despite different dimensionality, in linear systems the two measures are equivalent: [Formula: see text]. However, nonlinear model neurons produce different values for Z and [Formula: see text]. In particular, nonlinearities in the voltage equation produce a much larger difference between these two quantities than those in equations of recovery variables that describe activation and inactivation kinetics. Neurons are inherently nonlinear, and notably, with ionic currents that amplify resonance, the voltage clamp technique severely underestimates the current clamp response. We demonstrate this difference experimentally using the PD neurons in the crab stomatogastric ganglion. These findings are instructive for researchers who explore cellular mechanisms of neuronal oscillations.

Entities:  

Keywords:  Impedance profile; Neural oscillations; Resonance; Sub-threshold resonance

Mesh:

Year:  2019        PMID: 31286211      PMCID: PMC6689413          DOI: 10.1007/s00422-019-00802-z

Source DB:  PubMed          Journal:  Biol Cybern        ISSN: 0340-1200            Impact factor:   2.086


  61 in total

1.  Membrane resonance and subthreshold membrane oscillations in mesencephalic V neurons: participants in burst generation.

Authors:  N Wu; C F Hsiao; S H Chandler
Journal:  J Neurosci       Date:  2001-06-01       Impact factor: 6.167

2.  Dynamics of rat entorhinal cortex layer II and III cells: characteristics of membrane potential resonance at rest predict oscillation properties near threshold.

Authors:  I Erchova; G Kreck; U Heinemann; A V M Herz
Journal:  J Physiol       Date:  2004-07-22       Impact factor: 5.182

3.  Subthreshold resonance explains the frequency-dependent integration of periodic as well as random stimuli in the entorhinal cortex.

Authors:  Susanne Schreiber; Irina Erchova; Uwe Heinemann; Andreas V M Herz
Journal:  J Neurophysiol       Date:  2004-03-10       Impact factor: 2.714

4.  The h channel mediates location dependence and plasticity of intrinsic phase response in rat hippocampal neurons.

Authors:  Rishikesh Narayanan; Daniel Johnston
Journal:  J Neurosci       Date:  2008-05-28       Impact factor: 6.167

5.  Subthreshold oscillations and resonant behavior: two manifestations of the same mechanism.

Authors:  I Lampl; Y Yarom
Journal:  Neuroscience       Date:  1997-05       Impact factor: 3.590

6.  Distinct Co-Modulation Rules of Synapses and Voltage-Gated Currents Coordinate Interactions of Multiple Neuromodulators.

Authors:  Xinping Li; Dirk Bucher; Farzan Nadim
Journal:  J Neurosci       Date:  2018-08-20       Impact factor: 6.167

7.  Small-signal analysis of K+ conduction in squid axons.

Authors:  L E Moore; H M Fishman; D J Poussart
Journal:  J Membr Biol       Date:  1980-05-23       Impact factor: 1.843

8.  Frequency preference in two-dimensional neural models: a linear analysis of the interaction between resonant and amplifying currents.

Authors:  Horacio G Rotstein; Farzan Nadim
Journal:  J Comput Neurosci       Date:  2013-11-20       Impact factor: 1.621

9.  The membrane potential waveform of bursting pacemaker neurons is a predictor of their preferred frequency and the network cycle frequency.

Authors:  Hua-an Tseng; Farzan Nadim
Journal:  J Neurosci       Date:  2010-08-11       Impact factor: 6.167

10.  The ionic mechanism of membrane potential oscillations and membrane resonance in striatal LTS interneurons.

Authors:  S C Song; J A Beatty; C J Wilson
Journal:  J Neurophysiol       Date:  2016-07-20       Impact factor: 2.714

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  1 in total

Review 1.  The voltage and spiking responses of subthreshold resonant neurons to structured and fluctuating inputs: persistence and loss of resonance and variability.

Authors:  Rodrigo F O Pena; Horacio G Rotstein
Journal:  Biol Cybern       Date:  2022-01-17       Impact factor: 2.086

  1 in total

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