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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

Fractional differentiation by electrosensory pyramidal neurons achieves temporal decorrelation.(a) Schematic representation showing that the neural tuning function (middle) must oppose the decay in the stimulus power (left) in order to achieve a neural response that is constant (right). (b) Phase histograms showing the firing rate modulation in response to the stimulus (blue) for low (dashed red) and high (solid red) 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 (brown) sensitivity (top) and phase (bottom) obtained from sinusoidal stimuli (N=14). The solid orange lines show the gain and phase of the best-fit fractional derivative. (d) Predicted (orange) and actual (red) response power spectra to natural stimuli (N=14). The grey band shows 1 s.e.m. Inset: predicted (orange) and actual (red) response autocorrelation function. The grey band shows the 95% confidence interval around zero. (e,f) Predicted as a function of actual correlation time and white index, respectively.
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f2: Fractional differentiation by electrosensory pyramidal neurons achieves temporal decorrelation.(a) Schematic representation showing that the neural tuning function (middle) must oppose the decay in the stimulus power (left) in order to achieve a neural response that is constant (right). (b) Phase histograms showing the firing rate modulation in response to the stimulus (blue) for low (dashed red) and high (solid red) 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 (brown) sensitivity (top) and phase (bottom) obtained from sinusoidal stimuli (N=14). The solid orange lines show the gain and phase of the best-fit fractional derivative. (d) Predicted (orange) and actual (red) response power spectra to natural stimuli (N=14). The grey band shows 1 s.e.m. Inset: predicted (orange) and actual (red) response autocorrelation function. The grey band shows the 95% confidence interval around zero. (e,f) Predicted as a function of actual correlation time and white index, respectively.

Mentions: How is temporal whitening of natural stimuli by pyramidal neurons achieved? Theory posits that such whitening is achieved by ensuring that the neuron's tuning curve is matched to the statistics of natural input9. Neural sensitivity should then be highest for frequencies at which stimulus power is lowest. A simple derivation (see Methods) predicts that, in order to achieve temporal whitening of stimuli whose power decreases with exponent αstim= −0.8, neural sensitivity should increase as a power law with exponent αneuron=−αstim/2=0.4 (Fig. 2a).


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

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

Fractional differentiation by electrosensory pyramidal neurons achieves temporal decorrelation.(a) Schematic representation showing that the neural tuning function (middle) must oppose the decay in the stimulus power (left) in order to achieve a neural response that is constant (right). (b) Phase histograms showing the firing rate modulation in response to the stimulus (blue) for low (dashed red) and high (solid red) 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 (brown) sensitivity (top) and phase (bottom) obtained from sinusoidal stimuli (N=14). The solid orange lines show the gain and phase of the best-fit fractional derivative. (d) Predicted (orange) and actual (red) response power spectra to natural stimuli (N=14). The grey band shows 1 s.e.m. Inset: predicted (orange) and actual (red) response autocorrelation function. The grey band shows the 95% confidence interval around zero. (e,f) Predicted as a function of actual correlation time and white index, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC4837484&req=5

f2: Fractional differentiation by electrosensory pyramidal neurons achieves temporal decorrelation.(a) Schematic representation showing that the neural tuning function (middle) must oppose the decay in the stimulus power (left) in order to achieve a neural response that is constant (right). (b) Phase histograms showing the firing rate modulation in response to the stimulus (blue) for low (dashed red) and high (solid red) 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 (brown) sensitivity (top) and phase (bottom) obtained from sinusoidal stimuli (N=14). The solid orange lines show the gain and phase of the best-fit fractional derivative. (d) Predicted (orange) and actual (red) response power spectra to natural stimuli (N=14). The grey band shows 1 s.e.m. Inset: predicted (orange) and actual (red) response autocorrelation function. The grey band shows the 95% confidence interval around zero. (e,f) Predicted as a function of actual correlation time and white index, respectively.
Mentions: How is temporal whitening of natural stimuli by pyramidal neurons achieved? Theory posits that such whitening is achieved by ensuring that the neuron's tuning curve is matched to the statistics of natural input9. Neural sensitivity should then be highest for frequencies at which stimulus power is lowest. A simple derivation (see Methods) predicts that, in order to achieve temporal whitening of stimuli whose power decreases with exponent αstim= −0.8, neural sensitivity should increase as a power law with exponent αneuron=−αstim/2=0.4 (Fig. 2a).

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