Influence of subthreshold nonlinearities on signal-to-noise ratio and timing precision for small signals in neurons

Minimal model analysis

Gytis Svirskis, John Rinzel

Research output: Contribution to journalArticle

Abstract

Subthreshold voltage- and time-dependent conductances can subserve different roles in signal integration and action potential generation. Here, we use minimal models to demonstrate how a non-inactivating low-threshold outward current (IKLT) can enhance the precision of small-signal integration. Our integrate-and-fire models have only a few biophysical parameters, enabling a parametric study of IKLT's effects. IKLT increases the signal-to-noise ratio (SNR) for firing when a subthreshold 'signal' EPSP is delivered in the presence of weak random input. The increased SNR is due to the suppression of spontaneous firings to random input. In accordance, SNR grows as the EPSP amplitude increases. SNR also grows as the unitary synaptic current's time constant increases, leading to more effective suppression of spontaneous activity. Spike-triggered reverse correlation of the injected current indicates that, to reach spike threshold, a cell with IKLT requires a briefer time course of injected current. Consistent with this narrowed integration time window, IKLT enhances phase-locking, measured as vector strength, to a weak noisy and periodically modulated stimulus. Thus subthreshold negative feedback mediated by IKLT enhances temporal processing. An alternative suppression mechanism is voltage- and time-dependent inactivation of a low-threshold inward current. This feature in an integrate-and-fire model also shows SNR enhancement, in comparison with a case when the inward current is non-inactivating. Small-signal detection can be significantly improved in noisy neuronal systems by subthreshold negative feedback, serving to suppress false positives.

Original languageEnglish (US)
Pages (from-to)137-150
Number of pages14
JournalNetwork: Computation in Neural Systems
Volume14
Issue number1
DOIs
StatePublished - Feb 2003

Fingerprint

Signal-To-Noise Ratio
Neurons
Excitatory Postsynaptic Potentials
Action Potentials

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

@article{00b2678a12544b1eb803dc6ab51379b8,
title = "Influence of subthreshold nonlinearities on signal-to-noise ratio and timing precision for small signals in neurons: Minimal model analysis",
abstract = "Subthreshold voltage- and time-dependent conductances can subserve different roles in signal integration and action potential generation. Here, we use minimal models to demonstrate how a non-inactivating low-threshold outward current (IKLT) can enhance the precision of small-signal integration. Our integrate-and-fire models have only a few biophysical parameters, enabling a parametric study of IKLT's effects. IKLT increases the signal-to-noise ratio (SNR) for firing when a subthreshold 'signal' EPSP is delivered in the presence of weak random input. The increased SNR is due to the suppression of spontaneous firings to random input. In accordance, SNR grows as the EPSP amplitude increases. SNR also grows as the unitary synaptic current's time constant increases, leading to more effective suppression of spontaneous activity. Spike-triggered reverse correlation of the injected current indicates that, to reach spike threshold, a cell with IKLT requires a briefer time course of injected current. Consistent with this narrowed integration time window, IKLT enhances phase-locking, measured as vector strength, to a weak noisy and periodically modulated stimulus. Thus subthreshold negative feedback mediated by IKLT enhances temporal processing. An alternative suppression mechanism is voltage- and time-dependent inactivation of a low-threshold inward current. This feature in an integrate-and-fire model also shows SNR enhancement, in comparison with a case when the inward current is non-inactivating. Small-signal detection can be significantly improved in noisy neuronal systems by subthreshold negative feedback, serving to suppress false positives.",
author = "Gytis Svirskis and John Rinzel",
year = "2003",
month = "2",
doi = "10.1088/0954-898X/14/1/308",
language = "English (US)",
volume = "14",
pages = "137--150",
journal = "Network: Computation in Neural Systems",
issn = "0954-898X",
publisher = "Informa Healthcare",
number = "1",

}

TY - JOUR

T1 - Influence of subthreshold nonlinearities on signal-to-noise ratio and timing precision for small signals in neurons

T2 - Minimal model analysis

AU - Svirskis, Gytis

AU - Rinzel, John

PY - 2003/2

Y1 - 2003/2

N2 - Subthreshold voltage- and time-dependent conductances can subserve different roles in signal integration and action potential generation. Here, we use minimal models to demonstrate how a non-inactivating low-threshold outward current (IKLT) can enhance the precision of small-signal integration. Our integrate-and-fire models have only a few biophysical parameters, enabling a parametric study of IKLT's effects. IKLT increases the signal-to-noise ratio (SNR) for firing when a subthreshold 'signal' EPSP is delivered in the presence of weak random input. The increased SNR is due to the suppression of spontaneous firings to random input. In accordance, SNR grows as the EPSP amplitude increases. SNR also grows as the unitary synaptic current's time constant increases, leading to more effective suppression of spontaneous activity. Spike-triggered reverse correlation of the injected current indicates that, to reach spike threshold, a cell with IKLT requires a briefer time course of injected current. Consistent with this narrowed integration time window, IKLT enhances phase-locking, measured as vector strength, to a weak noisy and periodically modulated stimulus. Thus subthreshold negative feedback mediated by IKLT enhances temporal processing. An alternative suppression mechanism is voltage- and time-dependent inactivation of a low-threshold inward current. This feature in an integrate-and-fire model also shows SNR enhancement, in comparison with a case when the inward current is non-inactivating. Small-signal detection can be significantly improved in noisy neuronal systems by subthreshold negative feedback, serving to suppress false positives.

AB - Subthreshold voltage- and time-dependent conductances can subserve different roles in signal integration and action potential generation. Here, we use minimal models to demonstrate how a non-inactivating low-threshold outward current (IKLT) can enhance the precision of small-signal integration. Our integrate-and-fire models have only a few biophysical parameters, enabling a parametric study of IKLT's effects. IKLT increases the signal-to-noise ratio (SNR) for firing when a subthreshold 'signal' EPSP is delivered in the presence of weak random input. The increased SNR is due to the suppression of spontaneous firings to random input. In accordance, SNR grows as the EPSP amplitude increases. SNR also grows as the unitary synaptic current's time constant increases, leading to more effective suppression of spontaneous activity. Spike-triggered reverse correlation of the injected current indicates that, to reach spike threshold, a cell with IKLT requires a briefer time course of injected current. Consistent with this narrowed integration time window, IKLT enhances phase-locking, measured as vector strength, to a weak noisy and periodically modulated stimulus. Thus subthreshold negative feedback mediated by IKLT enhances temporal processing. An alternative suppression mechanism is voltage- and time-dependent inactivation of a low-threshold inward current. This feature in an integrate-and-fire model also shows SNR enhancement, in comparison with a case when the inward current is non-inactivating. Small-signal detection can be significantly improved in noisy neuronal systems by subthreshold negative feedback, serving to suppress false positives.

UR - http://www.scopus.com/inward/record.url?scp=0346914339&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0346914339&partnerID=8YFLogxK

U2 - 10.1088/0954-898X/14/1/308

DO - 10.1088/0954-898X/14/1/308

M3 - Article

VL - 14

SP - 137

EP - 150

JO - Network: Computation in Neural Systems

JF - Network: Computation in Neural Systems

SN - 0954-898X

IS - 1

ER -