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Sparse representation of sounds in the unanesthetized auditory cortex.

Hromádka T, Deweese MR, Zador AM - PLoS Biol. (2008)

Bottom Line: Interestingly, the overall population response was well described by a lognormal distribution, rather than the exponential distribution that is often reported.Our results represent, to our knowledge, the first quantitative evidence for sparse representations of sounds in the unanesthetized auditory cortex.Our results are compatible with a model in which most neurons are silent much of the time, and in which representations are composed of small dynamic subsets of highly active neurons.

View Article: PubMed Central - PubMed

Affiliation: Cold Spring Harbor Laboratory, Watson School of Biological Sciences, Cold Spring Harbor, New York, United States of America.

ABSTRACT
How do neuronal populations in the auditory cortex represent acoustic stimuli? Although sound-evoked neural responses in the anesthetized auditory cortex are mainly transient, recent experiments in the unanesthetized preparation have emphasized subpopulations with other response properties. To quantify the relative contributions of these different subpopulations in the awake preparation, we have estimated the representation of sounds across the neuronal population using a representative ensemble of stimuli. We used cell-attached recording with a glass electrode, a method for which single-unit isolation does not depend on neuronal activity, to quantify the fraction of neurons engaged by acoustic stimuli (tones, frequency modulated sweeps, white-noise bursts, and natural stimuli) in the primary auditory cortex of awake head-fixed rats. We find that the population response is sparse, with stimuli typically eliciting high firing rates (>20 spikes/second) in less than 5% of neurons at any instant. Some neurons had very low spontaneous firing rates (<0.01 spikes/second). At the other extreme, some neurons had driven rates in excess of 50 spikes/second. Interestingly, the overall population response was well described by a lognormal distribution, rather than the exponential distribution that is often reported. Our results represent, to our knowledge, the first quantitative evidence for sparse representations of sounds in the unanesthetized auditory cortex. Our results are compatible with a model in which most neurons are silent much of the time, and in which representations are composed of small dynamic subsets of highly active neurons.

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A Lognormal Distribution Provides a Better Fit to the Data than an Exponential Distribution(A) The cumulative density (top) and probability density (bottom) functions of the data (black points) are better fit by a lognormal distribution (dark gray line) than an exponential distribution (light gray line) for spontaneous firing rates. The mean and standard deviation of the lognormal fit were given by the mean and standard deviation of the distribution of (natural) logarithms of the firing rates. The mean of the exponential fit was given by the mean of the firing rate distribution.(B, C, and D) Same format as in (A) for early, late, and off epochs, respectively.
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pbio-0060016-g004: A Lognormal Distribution Provides a Better Fit to the Data than an Exponential Distribution(A) The cumulative density (top) and probability density (bottom) functions of the data (black points) are better fit by a lognormal distribution (dark gray line) than an exponential distribution (light gray line) for spontaneous firing rates. The mean and standard deviation of the lognormal fit were given by the mean and standard deviation of the distribution of (natural) logarithms of the firing rates. The mean of the exponential fit was given by the mean of the firing rate distribution.(B, C, and D) Same format as in (A) for early, late, and off epochs, respectively.

Mentions: The distribution of spontaneous firing rates across the population was remarkably well fit with a lognormal distribution—that is, the logarithm of the firing rates was well fit with a Gaussian distribution (Figure 3B and 3C). Because the lognormal distribution has a “heavy tail,” most spikes were generated by just a few neurons: About 16% of neurons—the subset of 23 neurons firing at higher than 9.5 sp/s—accounted for 50% of all spikes. The lognormal distribution fit better than the exponential distribution, particularly at low firing rates (Figure 4); because we were using cell-attached recording, we were confident that we were not undersampling the low-firing end of the distribution and that therefore this improved fit was real. Although lognormal distributions have widely been used to describe the ISI distributions from a single neuron, population responses are usually reported to be exponentially distributed [6,25,26]; this is, to our knowledge, the first report that firing rates across a population of neurons are lognormally distributed.


Sparse representation of sounds in the unanesthetized auditory cortex.

Hromádka T, Deweese MR, Zador AM - PLoS Biol. (2008)

A Lognormal Distribution Provides a Better Fit to the Data than an Exponential Distribution(A) The cumulative density (top) and probability density (bottom) functions of the data (black points) are better fit by a lognormal distribution (dark gray line) than an exponential distribution (light gray line) for spontaneous firing rates. The mean and standard deviation of the lognormal fit were given by the mean and standard deviation of the distribution of (natural) logarithms of the firing rates. The mean of the exponential fit was given by the mean of the firing rate distribution.(B, C, and D) Same format as in (A) for early, late, and off epochs, respectively.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2214813&req=5

pbio-0060016-g004: A Lognormal Distribution Provides a Better Fit to the Data than an Exponential Distribution(A) The cumulative density (top) and probability density (bottom) functions of the data (black points) are better fit by a lognormal distribution (dark gray line) than an exponential distribution (light gray line) for spontaneous firing rates. The mean and standard deviation of the lognormal fit were given by the mean and standard deviation of the distribution of (natural) logarithms of the firing rates. The mean of the exponential fit was given by the mean of the firing rate distribution.(B, C, and D) Same format as in (A) for early, late, and off epochs, respectively.
Mentions: The distribution of spontaneous firing rates across the population was remarkably well fit with a lognormal distribution—that is, the logarithm of the firing rates was well fit with a Gaussian distribution (Figure 3B and 3C). Because the lognormal distribution has a “heavy tail,” most spikes were generated by just a few neurons: About 16% of neurons—the subset of 23 neurons firing at higher than 9.5 sp/s—accounted for 50% of all spikes. The lognormal distribution fit better than the exponential distribution, particularly at low firing rates (Figure 4); because we were using cell-attached recording, we were confident that we were not undersampling the low-firing end of the distribution and that therefore this improved fit was real. Although lognormal distributions have widely been used to describe the ISI distributions from a single neuron, population responses are usually reported to be exponentially distributed [6,25,26]; this is, to our knowledge, the first report that firing rates across a population of neurons are lognormally distributed.

Bottom Line: Interestingly, the overall population response was well described by a lognormal distribution, rather than the exponential distribution that is often reported.Our results represent, to our knowledge, the first quantitative evidence for sparse representations of sounds in the unanesthetized auditory cortex.Our results are compatible with a model in which most neurons are silent much of the time, and in which representations are composed of small dynamic subsets of highly active neurons.

View Article: PubMed Central - PubMed

Affiliation: Cold Spring Harbor Laboratory, Watson School of Biological Sciences, Cold Spring Harbor, New York, United States of America.

ABSTRACT
How do neuronal populations in the auditory cortex represent acoustic stimuli? Although sound-evoked neural responses in the anesthetized auditory cortex are mainly transient, recent experiments in the unanesthetized preparation have emphasized subpopulations with other response properties. To quantify the relative contributions of these different subpopulations in the awake preparation, we have estimated the representation of sounds across the neuronal population using a representative ensemble of stimuli. We used cell-attached recording with a glass electrode, a method for which single-unit isolation does not depend on neuronal activity, to quantify the fraction of neurons engaged by acoustic stimuli (tones, frequency modulated sweeps, white-noise bursts, and natural stimuli) in the primary auditory cortex of awake head-fixed rats. We find that the population response is sparse, with stimuli typically eliciting high firing rates (>20 spikes/second) in less than 5% of neurons at any instant. Some neurons had very low spontaneous firing rates (<0.01 spikes/second). At the other extreme, some neurons had driven rates in excess of 50 spikes/second. Interestingly, the overall population response was well described by a lognormal distribution, rather than the exponential distribution that is often reported. Our results represent, to our knowledge, the first quantitative evidence for sparse representations of sounds in the unanesthetized auditory cortex. Our results are compatible with a model in which most neurons are silent much of the time, and in which representations are composed of small dynamic subsets of highly active neurons.

Show MeSH