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Noise normalizes firing output of mouse lateral geniculate nucleus neurons.

Wijesinghe R, Solomon SG, Camp AJ - PLoS ONE (2013)

Bottom Line: As expected, injection of current noise via the recording pipette induces shifts in neuronal gain that are dependent on the amplitude of current noise, such that larger shifts in gain are observed in response to larger amplitude noise injections.In contrast, when the cortical feedback network was activated, only multiplicative gain changes were observed.These network activation-dependent changes were associated with reductions in the slow afterhyperpolarization (sAHP), and were mediated at least in part, by T-type calcium channels.

View Article: PubMed Central - PubMed

Affiliation: Sydney Medical School, School of Medical Sciences and Bosch Institute, The University of Sydney, New South Wales, Australia.

ABSTRACT
The output of individual neurons is dependent on both synaptic and intrinsic membrane properties. While it is clear that the response of an individual neuron can be facilitated or inhibited based on the summation of its constituent synaptic inputs, it is not clear whether subthreshold activity, (e.g. synaptic "noise"--fluctuations that do not change the mean membrane potential) also serve a function in the control of neuronal output. Here we studied this by making whole-cell patch-clamp recordings from 29 mouse thalamocortical relay (TC) neurons. For each neuron we measured neuronal gain in response to either injection of current noise, or activation of the metabotropic glutamate receptor-mediated cortical feedback network (synaptic noise). As expected, injection of current noise via the recording pipette induces shifts in neuronal gain that are dependent on the amplitude of current noise, such that larger shifts in gain are observed in response to larger amplitude noise injections. Importantly we show that shifts in neuronal gain are also dependent on the intrinsic sensitivity of the neuron tested, such that the gain of intrinsically sensitive neurons is attenuated divisively in response to current noise, while the gain of insensitive neurons is facilitated multiplicatively by injection of current noise- effectively normalizing the output of the dLGN as a whole. In contrast, when the cortical feedback network was activated, only multiplicative gain changes were observed. These network activation-dependent changes were associated with reductions in the slow afterhyperpolarization (sAHP), and were mediated at least in part, by T-type calcium channels. Together, this suggests that TC neurons have the machinery necessary to compute multiple output solutions to a given set of stimuli depending on the current level of network stimulation.

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Related in: MedlinePlus

Noise induces both additive and multiplicative gain changesA. Shows for a typical TC cell the f-I relationships plotted at different levels of current noise (σn, where σ is the standard deviation of the membrane potential in response to a ‘noisy’ current pulse with a mean current of 0 pA, and n represents the standard deviation of the injected current noise). In this example, the highest level of noise significantly increased the gain (0.05 to 0.39 Hz/pA; multiplicative gain change, indicated by an increase in the slope) and decreased the threshold (160 to 140 pA; additive gain change, indicated by a shift to the left) of this cell in comparison to control conditions. B. Gains averaged across the sample population plotted against noise level. On average, increasing levels of noise increased the gain of TC cells. Data points were well fit by an inverse exponential function, indicating that increases in gain saturate at high noise levels. C. Increasing levels of noise reduced the threshold of TC cells. As in B, this reduction saturated at high noise levels (between 1.0 and 1.5).
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pone-0057961-g004: Noise induces both additive and multiplicative gain changesA. Shows for a typical TC cell the f-I relationships plotted at different levels of current noise (σn, where σ is the standard deviation of the membrane potential in response to a ‘noisy’ current pulse with a mean current of 0 pA, and n represents the standard deviation of the injected current noise). In this example, the highest level of noise significantly increased the gain (0.05 to 0.39 Hz/pA; multiplicative gain change, indicated by an increase in the slope) and decreased the threshold (160 to 140 pA; additive gain change, indicated by a shift to the left) of this cell in comparison to control conditions. B. Gains averaged across the sample population plotted against noise level. On average, increasing levels of noise increased the gain of TC cells. Data points were well fit by an inverse exponential function, indicating that increases in gain saturate at high noise levels. C. Increasing levels of noise reduced the threshold of TC cells. As in B, this reduction saturated at high noise levels (between 1.0 and 1.5).

Mentions: The output of individual cells is the eventual product of transformations imposed by synaptic inputs on intrinsic membrane properties. As a simple substitute for network activity, we asked how adding noise to the current pulse, simulating the addition of a background synaptic barrage, altered the gain of TC cells. Figure 4A shows the f-I relationship for a single TC cell in response to different levels of current noise. In this example, gain increased with increasing levels of noise (0.05 Hz/pA at σ0  =  0.05 mV, 0.39 Hz/pA at highest noise level, σ50  =  1.57 mV, Fig. 4A inset) and threshold decreased (σ0: 170 pA, σ50: 140 pA). Across our sample of cells the addition of noise increased gain from 0.27 ± 0.04 Hz/pA at σ0 =  0.53 ± 0.05 mV, to 0.41 ± 0.02 Hz/pA at σ50  =  1.57 ± 0.09 mV (n  =  18, p < 0.01, Fig. 4B). The increase in gain is consistent with a multiplicative transformation of neuronal output. Meanwhile, threshold decreased on average, from 160 ± 8.6 pA at σ0, to 122 ± 5.2 pA at σ50 (n  =  18, p < 0.001, Fig. 4C). This represents a leftward shift of the f-I curve (see Fig. 4A), and unlike the change in gain is consistent with an additive process [33].


Noise normalizes firing output of mouse lateral geniculate nucleus neurons.

Wijesinghe R, Solomon SG, Camp AJ - PLoS ONE (2013)

Noise induces both additive and multiplicative gain changesA. Shows for a typical TC cell the f-I relationships plotted at different levels of current noise (σn, where σ is the standard deviation of the membrane potential in response to a ‘noisy’ current pulse with a mean current of 0 pA, and n represents the standard deviation of the injected current noise). In this example, the highest level of noise significantly increased the gain (0.05 to 0.39 Hz/pA; multiplicative gain change, indicated by an increase in the slope) and decreased the threshold (160 to 140 pA; additive gain change, indicated by a shift to the left) of this cell in comparison to control conditions. B. Gains averaged across the sample population plotted against noise level. On average, increasing levels of noise increased the gain of TC cells. Data points were well fit by an inverse exponential function, indicating that increases in gain saturate at high noise levels. C. Increasing levels of noise reduced the threshold of TC cells. As in B, this reduction saturated at high noise levels (between 1.0 and 1.5).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3585224&req=5

pone-0057961-g004: Noise induces both additive and multiplicative gain changesA. Shows for a typical TC cell the f-I relationships plotted at different levels of current noise (σn, where σ is the standard deviation of the membrane potential in response to a ‘noisy’ current pulse with a mean current of 0 pA, and n represents the standard deviation of the injected current noise). In this example, the highest level of noise significantly increased the gain (0.05 to 0.39 Hz/pA; multiplicative gain change, indicated by an increase in the slope) and decreased the threshold (160 to 140 pA; additive gain change, indicated by a shift to the left) of this cell in comparison to control conditions. B. Gains averaged across the sample population plotted against noise level. On average, increasing levels of noise increased the gain of TC cells. Data points were well fit by an inverse exponential function, indicating that increases in gain saturate at high noise levels. C. Increasing levels of noise reduced the threshold of TC cells. As in B, this reduction saturated at high noise levels (between 1.0 and 1.5).
Mentions: The output of individual cells is the eventual product of transformations imposed by synaptic inputs on intrinsic membrane properties. As a simple substitute for network activity, we asked how adding noise to the current pulse, simulating the addition of a background synaptic barrage, altered the gain of TC cells. Figure 4A shows the f-I relationship for a single TC cell in response to different levels of current noise. In this example, gain increased with increasing levels of noise (0.05 Hz/pA at σ0  =  0.05 mV, 0.39 Hz/pA at highest noise level, σ50  =  1.57 mV, Fig. 4A inset) and threshold decreased (σ0: 170 pA, σ50: 140 pA). Across our sample of cells the addition of noise increased gain from 0.27 ± 0.04 Hz/pA at σ0 =  0.53 ± 0.05 mV, to 0.41 ± 0.02 Hz/pA at σ50  =  1.57 ± 0.09 mV (n  =  18, p < 0.01, Fig. 4B). The increase in gain is consistent with a multiplicative transformation of neuronal output. Meanwhile, threshold decreased on average, from 160 ± 8.6 pA at σ0, to 122 ± 5.2 pA at σ50 (n  =  18, p < 0.001, Fig. 4C). This represents a leftward shift of the f-I curve (see Fig. 4A), and unlike the change in gain is consistent with an additive process [33].

Bottom Line: As expected, injection of current noise via the recording pipette induces shifts in neuronal gain that are dependent on the amplitude of current noise, such that larger shifts in gain are observed in response to larger amplitude noise injections.In contrast, when the cortical feedback network was activated, only multiplicative gain changes were observed.These network activation-dependent changes were associated with reductions in the slow afterhyperpolarization (sAHP), and were mediated at least in part, by T-type calcium channels.

View Article: PubMed Central - PubMed

Affiliation: Sydney Medical School, School of Medical Sciences and Bosch Institute, The University of Sydney, New South Wales, Australia.

ABSTRACT
The output of individual neurons is dependent on both synaptic and intrinsic membrane properties. While it is clear that the response of an individual neuron can be facilitated or inhibited based on the summation of its constituent synaptic inputs, it is not clear whether subthreshold activity, (e.g. synaptic "noise"--fluctuations that do not change the mean membrane potential) also serve a function in the control of neuronal output. Here we studied this by making whole-cell patch-clamp recordings from 29 mouse thalamocortical relay (TC) neurons. For each neuron we measured neuronal gain in response to either injection of current noise, or activation of the metabotropic glutamate receptor-mediated cortical feedback network (synaptic noise). As expected, injection of current noise via the recording pipette induces shifts in neuronal gain that are dependent on the amplitude of current noise, such that larger shifts in gain are observed in response to larger amplitude noise injections. Importantly we show that shifts in neuronal gain are also dependent on the intrinsic sensitivity of the neuron tested, such that the gain of intrinsically sensitive neurons is attenuated divisively in response to current noise, while the gain of insensitive neurons is facilitated multiplicatively by injection of current noise- effectively normalizing the output of the dLGN as a whole. In contrast, when the cortical feedback network was activated, only multiplicative gain changes were observed. These network activation-dependent changes were associated with reductions in the slow afterhyperpolarization (sAHP), and were mediated at least in part, by T-type calcium channels. Together, this suggests that TC neurons have the machinery necessary to compute multiple output solutions to a given set of stimuli depending on the current level of network stimulation.

Show MeSH
Related in: MedlinePlus