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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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Noise normalises gain changes.A. Gain changes were not uniform within the recorded population, as noise reduced gain in cells with initially high gains (n  =  5, open circles), and increased gain in those with low initial gains (n  =  13, closed circles). B. Histograms of gains across the sample population (n  =  18) under control conditions (dashed line) and for the highest level of noise (σ50, solid line). Note the sharper distribution of gains under noisy conditions. C. The standard deviation of the average of gains across the population, plotted against the corresponding noise level. The standard deviation is reduced by 52% at high noise levels. Data were fit with an inverse exponential function.
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pone-0057961-g005: Noise normalises gain changes.A. Gain changes were not uniform within the recorded population, as noise reduced gain in cells with initially high gains (n  =  5, open circles), and increased gain in those with low initial gains (n  =  13, closed circles). B. Histograms of gains across the sample population (n  =  18) under control conditions (dashed line) and for the highest level of noise (σ50, solid line). Note the sharper distribution of gains under noisy conditions. C. The standard deviation of the average of gains across the population, plotted against the corresponding noise level. The standard deviation is reduced by 52% at high noise levels. Data were fit with an inverse exponential function.

Mentions: Although the addition of noise on average increased the gain of TC cells, in 5 of 18 cells tested the gain significantly decreased (for this sample, σ0: 0.50 ± 0.05 Hz/pA, σ50: 0.44 ± 0.03 Hz/pA; n  =  5, p  =  0.02). Figure 5A plots gain as a function of noise amplitude for those cells where gain increased (solid circles) and those where it decreased (open circles). The impact of the noise depends on the initial gain of the cell, so that those with high gain in the absence of noise are attenuated by noise and vice versa. To further characterise this, we plotted histograms for the distribution of gains under both control and noisy conditions (Figure 5B). Under noisy conditions, the range of gains was 34% smaller than under control conditions (noise: 0.20–0.55 Hz/pA, control: 0.0–0.68 Hz/pA), suggesting a narrowing of the distribution. The standard deviation (SD) of the distribution in the presence of noise was correspondingly reduced by 52% when compared to measurements obtained without noise (noise: 0.08 Hz/pA, control: 0.19 Hz/pA; Fig. 5C). These changes indicate that noise decreases the variability in sensitivity between cells. This noise-induced reduction in variability was also evident in the thresholds: the range decreased by 30% (noise: 70 pA, control: 100 pA), and the SD of the distribution decreased by 39% (noise: 22 pA, control: 36 pA). No other recorded parameters distinguished cells with high and low gain.


Noise normalizes firing output of mouse lateral geniculate nucleus neurons.

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

Noise normalises gain changes.A. Gain changes were not uniform within the recorded population, as noise reduced gain in cells with initially high gains (n  =  5, open circles), and increased gain in those with low initial gains (n  =  13, closed circles). B. Histograms of gains across the sample population (n  =  18) under control conditions (dashed line) and for the highest level of noise (σ50, solid line). Note the sharper distribution of gains under noisy conditions. C. The standard deviation of the average of gains across the population, plotted against the corresponding noise level. The standard deviation is reduced by 52% at high noise levels. Data were fit with an inverse exponential function.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0057961-g005: Noise normalises gain changes.A. Gain changes were not uniform within the recorded population, as noise reduced gain in cells with initially high gains (n  =  5, open circles), and increased gain in those with low initial gains (n  =  13, closed circles). B. Histograms of gains across the sample population (n  =  18) under control conditions (dashed line) and for the highest level of noise (σ50, solid line). Note the sharper distribution of gains under noisy conditions. C. The standard deviation of the average of gains across the population, plotted against the corresponding noise level. The standard deviation is reduced by 52% at high noise levels. Data were fit with an inverse exponential function.
Mentions: Although the addition of noise on average increased the gain of TC cells, in 5 of 18 cells tested the gain significantly decreased (for this sample, σ0: 0.50 ± 0.05 Hz/pA, σ50: 0.44 ± 0.03 Hz/pA; n  =  5, p  =  0.02). Figure 5A plots gain as a function of noise amplitude for those cells where gain increased (solid circles) and those where it decreased (open circles). The impact of the noise depends on the initial gain of the cell, so that those with high gain in the absence of noise are attenuated by noise and vice versa. To further characterise this, we plotted histograms for the distribution of gains under both control and noisy conditions (Figure 5B). Under noisy conditions, the range of gains was 34% smaller than under control conditions (noise: 0.20–0.55 Hz/pA, control: 0.0–0.68 Hz/pA), suggesting a narrowing of the distribution. The standard deviation (SD) of the distribution in the presence of noise was correspondingly reduced by 52% when compared to measurements obtained without noise (noise: 0.08 Hz/pA, control: 0.19 Hz/pA; Fig. 5C). These changes indicate that noise decreases the variability in sensitivity between cells. This noise-induced reduction in variability was also evident in the thresholds: the range decreased by 30% (noise: 70 pA, control: 100 pA), and the SD of the distribution decreased by 39% (noise: 22 pA, control: 36 pA). No other recorded parameters distinguished cells with high and low gain.

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