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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.


Our model predicts that adaptation strength is critical to ensure efficient processing of natural stimuli.(a,b) Model gain and phase as a function of frequency for different amounts of adaptation, respectively. For each amount of adaptation, the circles show the values obtained from numerical simulation and the dashed lines those from the best-fit fractional derivative model. Note the progressive steepening of the gain curve as well as the increase in phase as adaptation is increased (black arrows). (c) Neural exponent αneuron as a function of adaptation showing values obtained from numerical simulation (black circles) and theoretical prediction (dashed line). (d) White index computed in response to a natural stimulus with exponent αstim=−0.8 as a function of adaptation showing values obtained from numerical simulation (black circles) and theoretical prediction (dashed line).
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f4: Our model predicts that adaptation strength is critical to ensure efficient processing of natural stimuli.(a,b) Model gain and phase as a function of frequency for different amounts of adaptation, respectively. For each amount of adaptation, the circles show the values obtained from numerical simulation and the dashed lines those from the best-fit fractional derivative model. Note the progressive steepening of the gain curve as well as the increase in phase as adaptation is increased (black arrows). (c) Neural exponent αneuron as a function of adaptation showing values obtained from numerical simulation (black circles) and theoretical prediction (dashed line). (d) White index computed in response to a natural stimulus with exponent αstim=−0.8 as a function of adaptation showing values obtained from numerical simulation (black circles) and theoretical prediction (dashed line).

Mentions: To understand how adaptation can lead to efficient processing of natural stimuli, we next systematically varied the strength of the adaptation current in our model. We found that, without adaptation, our model displayed constant sensitivity and no phase lead in response to envelope stimuli (light green curves in Fig. 4a,b). Increasing the adaptation strength led to sensitivity curves which increased more steeply as a function of frequency and furthermore increased phase lead (compare light and dark green curves Fig. 4a,b), consistent with increases in the neural exponent αneuron (Fig. 4c). These results have important implications as they predict that, for a given adaptation strength, our model can only achieve temporal decorrelation/whitening of stimuli whose power decays with a given exponent. This was verified by plotting the whitening index for naturalistic envelope stimuli (that is, αstim=−0.8) as a function of the adaptation strength. Indeed, both lower and higher adaptation strength led to tuning curves that were not matched to natural stimulus statistics and lowered coding efficiency as quantified by lower white index values (Fig. 4d).


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

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

Our model predicts that adaptation strength is critical to ensure efficient processing of natural stimuli.(a,b) Model gain and phase as a function of frequency for different amounts of adaptation, respectively. For each amount of adaptation, the circles show the values obtained from numerical simulation and the dashed lines those from the best-fit fractional derivative model. Note the progressive steepening of the gain curve as well as the increase in phase as adaptation is increased (black arrows). (c) Neural exponent αneuron as a function of adaptation showing values obtained from numerical simulation (black circles) and theoretical prediction (dashed line). (d) White index computed in response to a natural stimulus with exponent αstim=−0.8 as a function of adaptation showing values obtained from numerical simulation (black circles) and theoretical prediction (dashed line).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f4: Our model predicts that adaptation strength is critical to ensure efficient processing of natural stimuli.(a,b) Model gain and phase as a function of frequency for different amounts of adaptation, respectively. For each amount of adaptation, the circles show the values obtained from numerical simulation and the dashed lines those from the best-fit fractional derivative model. Note the progressive steepening of the gain curve as well as the increase in phase as adaptation is increased (black arrows). (c) Neural exponent αneuron as a function of adaptation showing values obtained from numerical simulation (black circles) and theoretical prediction (dashed line). (d) White index computed in response to a natural stimulus with exponent αstim=−0.8 as a function of adaptation showing values obtained from numerical simulation (black circles) and theoretical prediction (dashed line).
Mentions: To understand how adaptation can lead to efficient processing of natural stimuli, we next systematically varied the strength of the adaptation current in our model. We found that, without adaptation, our model displayed constant sensitivity and no phase lead in response to envelope stimuli (light green curves in Fig. 4a,b). Increasing the adaptation strength led to sensitivity curves which increased more steeply as a function of frequency and furthermore increased phase lead (compare light and dark green curves Fig. 4a,b), consistent with increases in the neural exponent αneuron (Fig. 4c). These results have important implications as they predict that, for a given adaptation strength, our model can only achieve temporal decorrelation/whitening of stimuli whose power decays with a given exponent. This was verified by plotting the whitening index for naturalistic envelope stimuli (that is, αstim=−0.8) as a function of the adaptation strength. Indeed, both lower and higher adaptation strength led to tuning curves that were not matched to natural stimulus statistics and lowered coding efficiency as quantified by lower white index values (Fig. 4d).

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.