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Temporal decorrelation by SK channels enables efficient neural coding and perception of natural stimuli.

Huang CG, Zhang ZD, Chacron MJ - Nat Commun (2016)

Bottom Line: However, the mechanisms by which such efficient processing is achieved, and the consequences for perception and behaviour remain poorly understood.Specifically, these channels allow for the high-pass filtering of sensory input, thereby removing temporal correlations or, equivalently, whitening frequency response power.Our results thus demonstrate a novel mechanism by which the nervous system can implement efficient processing and perception of natural sensory input that is likely to be shared across systems and species.

View Article: PubMed Central - PubMed

Affiliation: Department of Physiology, McGill University, 3655 Sir William Osler, Montreal, Quebec, Canada H3G 1Y6.

ABSTRACT
It is commonly assumed that neural systems efficiently process natural sensory input. However, the mechanisms by which such efficient processing is achieved, and the consequences for perception and behaviour remain poorly understood. Here we show that small conductance calcium-activated potassium (SK) channels enable efficient neural processing and perception of natural stimuli. Specifically, these channels allow for the high-pass filtering of sensory input, thereby removing temporal correlations or, equivalently, whitening frequency response power. Varying the degree of adaptation through pharmacological manipulation of SK channels reduced efficiency of coding of natural stimuli, which in turn gave rise to predictable changes in behavioural responses that were no longer matched to natural stimulus statistics. Our results thus demonstrate a novel mechanism by which the nervous system can implement efficient processing and perception of natural sensory input that is likely to be shared across systems and species.

No MeSH data available.


Related in: MedlinePlus

A simple model with power law adaptation implements temporal decorrelation by fractional differentiation.(a) Model schematic representation in which the stimulus (blue) is fed to a leaky integrate-and-fire (LIF) neuron model with an adaptation kernel that decays as a power law as a function of time. The spiking output of the model (red) was analysed in the same manner as the experimental data. (b) Phase histograms showing the firing rate modulation in response to the stimulus (blue) for low (dashed green) and high (solid green) envelope frequencies. The bands and vertical arrows show the amplitudes of the best sinusoidal fits (not shown for clarity) for both frequencies, which are used to compute gain. The horizontal arrows show the phase shift between the stimulus and the firing rate modulation signal. (c) Population-averaged sensitivity (top) and phase (bottom) for the data (brown) and our LIF model (green) obtained for sinusoidal stimuli. (d) Response power spectra to natural stimulation for our experimental data (red) and LIF model (green). The grey band shows 1 s.e.m. for the experimental data. Inset: response autocorrelation function to natural stimulation for our experimental data (red) and LIF model (green). The grey band shows the 95% confidence interval around zero for the experimental data. (e,f) Population-averaged values obtained from experimental data (red) and for our LIF model (green) for correlation time and white index, respectively.
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f3: A simple model with power law adaptation implements temporal decorrelation by fractional differentiation.(a) Model schematic representation in which the stimulus (blue) is fed to a leaky integrate-and-fire (LIF) neuron model with an adaptation kernel that decays as a power law as a function of time. The spiking output of the model (red) was analysed in the same manner as the experimental data. (b) Phase histograms showing the firing rate modulation in response to the stimulus (blue) for low (dashed green) and high (solid green) envelope frequencies. The bands and vertical arrows show the amplitudes of the best sinusoidal fits (not shown for clarity) for both frequencies, which are used to compute gain. The horizontal arrows show the phase shift between the stimulus and the firing rate modulation signal. (c) Population-averaged sensitivity (top) and phase (bottom) for the data (brown) and our LIF model (green) obtained for sinusoidal stimuli. (d) Response power spectra to natural stimulation for our experimental data (red) and LIF model (green). The grey band shows 1 s.e.m. for the experimental data. Inset: response autocorrelation function to natural stimulation for our experimental data (red) and LIF model (green). The grey band shows the 95% confidence interval around zero for the experimental data. (e,f) Population-averaged values obtained from experimental data (red) and for our LIF model (green) for correlation time and white index, respectively.

Mentions: To gain insight into the mechanism which enables pyramidal neurons to efficiently process natural stimuli through fractional differentiation, we built a simple model based on the leaky integrate-and-fire formalism that included a spike-activated adaptation current that decayed as a power law in the absence of firing37, see Methods (Fig. 3a). The output model spike train was analysed in the same way as our experimental data. Numerical simulation revealed that this simple model accurately reproduced our experimental data (compare Figs 2b and 3b). Indeed, the model neuron's sensitivity and phase closely matched those obtained experimentally (Fig. 3c). Importantly, the model also accurately reproduced temporal whitening in response to naturalistic stimuli (Fig. 3d) as quantified by correlation time (Fig. 3e) and white index (Fig. 3f).


Temporal decorrelation by SK channels enables efficient neural coding and perception of natural stimuli.

Huang CG, Zhang ZD, Chacron MJ - Nat Commun (2016)

A simple model with power law adaptation implements temporal decorrelation by fractional differentiation.(a) Model schematic representation in which the stimulus (blue) is fed to a leaky integrate-and-fire (LIF) neuron model with an adaptation kernel that decays as a power law as a function of time. The spiking output of the model (red) was analysed in the same manner as the experimental data. (b) Phase histograms showing the firing rate modulation in response to the stimulus (blue) for low (dashed green) and high (solid green) envelope frequencies. The bands and vertical arrows show the amplitudes of the best sinusoidal fits (not shown for clarity) for both frequencies, which are used to compute gain. The horizontal arrows show the phase shift between the stimulus and the firing rate modulation signal. (c) Population-averaged sensitivity (top) and phase (bottom) for the data (brown) and our LIF model (green) obtained for sinusoidal stimuli. (d) Response power spectra to natural stimulation for our experimental data (red) and LIF model (green). The grey band shows 1 s.e.m. for the experimental data. Inset: response autocorrelation function to natural stimulation for our experimental data (red) and LIF model (green). The grey band shows the 95% confidence interval around zero for the experimental data. (e,f) Population-averaged values obtained from experimental data (red) and for our LIF model (green) for correlation time and white index, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4837484&req=5

f3: A simple model with power law adaptation implements temporal decorrelation by fractional differentiation.(a) Model schematic representation in which the stimulus (blue) is fed to a leaky integrate-and-fire (LIF) neuron model with an adaptation kernel that decays as a power law as a function of time. The spiking output of the model (red) was analysed in the same manner as the experimental data. (b) Phase histograms showing the firing rate modulation in response to the stimulus (blue) for low (dashed green) and high (solid green) envelope frequencies. The bands and vertical arrows show the amplitudes of the best sinusoidal fits (not shown for clarity) for both frequencies, which are used to compute gain. The horizontal arrows show the phase shift between the stimulus and the firing rate modulation signal. (c) Population-averaged sensitivity (top) and phase (bottom) for the data (brown) and our LIF model (green) obtained for sinusoidal stimuli. (d) Response power spectra to natural stimulation for our experimental data (red) and LIF model (green). The grey band shows 1 s.e.m. for the experimental data. Inset: response autocorrelation function to natural stimulation for our experimental data (red) and LIF model (green). The grey band shows the 95% confidence interval around zero for the experimental data. (e,f) Population-averaged values obtained from experimental data (red) and for our LIF model (green) for correlation time and white index, respectively.
Mentions: To gain insight into the mechanism which enables pyramidal neurons to efficiently process natural stimuli through fractional differentiation, we built a simple model based on the leaky integrate-and-fire formalism that included a spike-activated adaptation current that decayed as a power law in the absence of firing37, see Methods (Fig. 3a). The output model spike train was analysed in the same way as our experimental data. Numerical simulation revealed that this simple model accurately reproduced our experimental data (compare Figs 2b and 3b). Indeed, the model neuron's sensitivity and phase closely matched those obtained experimentally (Fig. 3c). Importantly, the model also accurately reproduced temporal whitening in response to naturalistic stimuli (Fig. 3d) as quantified by correlation time (Fig. 3e) and white index (Fig. 3f).

Bottom Line: However, the mechanisms by which such efficient processing is achieved, and the consequences for perception and behaviour remain poorly understood.Specifically, these channels allow for the high-pass filtering of sensory input, thereby removing temporal correlations or, equivalently, whitening frequency response power.Our results thus demonstrate a novel mechanism by which the nervous system can implement efficient processing and perception of natural sensory input that is likely to be shared across systems and species.

View Article: PubMed Central - PubMed

Affiliation: Department of Physiology, McGill University, 3655 Sir William Osler, Montreal, Quebec, Canada H3G 1Y6.

ABSTRACT
It is commonly assumed that neural systems efficiently process natural sensory input. However, the mechanisms by which such efficient processing is achieved, and the consequences for perception and behaviour remain poorly understood. Here we show that small conductance calcium-activated potassium (SK) channels enable efficient neural processing and perception of natural stimuli. Specifically, these channels allow for the high-pass filtering of sensory input, thereby removing temporal correlations or, equivalently, whitening frequency response power. Varying the degree of adaptation through pharmacological manipulation of SK channels reduced efficiency of coding of natural stimuli, which in turn gave rise to predictable changes in behavioural responses that were no longer matched to natural stimulus statistics. Our results thus demonstrate a novel mechanism by which the nervous system can implement efficient processing and perception of natural sensory input that is likely to be shared across systems and species.

No MeSH data available.


Related in: MedlinePlus