Neural population coding of sound level adapts to stimulus statistics

2005 ◽  
Vol 8 (12) ◽  
pp. 1684-1689 ◽  
Author(s):  
Isabel Dean ◽  
Nicol S Harper ◽  
David McAlpine
Keyword(s):  
Sound Level ◽  
Population Coding ◽  
Neural Population

scholarly journals L-type calcium channels refine the neural population code of sound level

2016 ◽  
Vol 116 (6) ◽  
pp. 2550-2563 ◽  
Author(s):  
Calum Alex Grimsley ◽  
David Brian Green ◽  
Shobhana Sivaramakrishnan
Keyword(s):  
Calcium Channels ◽  
Calcium Current ◽  
Dynamic Range ◽  
Sound Level ◽  
Neural Population ◽  
Total Calcium ◽  
Local Networks ◽  
Sound Levels ◽  
Local Circuits

The coding of sound level by ensembles of neurons improves the accuracy with which listeners identify how loud a sound is. In the auditory system, the rate at which neurons fire in response to changes in sound level is shaped by local networks. Voltage-gated conductances alter local output by regulating neuronal firing, but their role in modulating responses to sound level is unclear. We tested the effects of L-type calcium channels (CaL: CaV1.1–1.4) on sound-level coding in the central nucleus of the inferior colliculus (ICC) in the auditory midbrain. We characterized the contribution of CaL to the total calcium current in brain slices and then examined its effects on rate-level functions (RLFs) in vivo using single-unit recordings in awake mice. CaL is a high-threshold current and comprises ∼50% of the total calcium current in ICC neurons. In vivo, CaL activates at sound levels that evoke high firing rates. In RLFs that increase monotonically with sound level, CaL boosts spike rates at high sound levels and increases the maximum firing rate achieved. In different populations of RLFs that change nonmonotonically with sound level, CaL either suppresses or enhances firing at sound levels that evoke maximum firing. CaL multiplies the gain of monotonic RLFs with dynamic range and divides the gain of nonmonotonic RLFs with the width of the RLF. These results suggest that a single broad class of calcium channels activates enhancing and suppressing local circuits to regulate the sensitivity of neuronal populations to sound level.

Sound frequency representation in primary auditory cortex is level tolerant for moderately loud, complex sounds

2011 ◽  
Vol 106 (2) ◽  
pp. 1016-1027 ◽  
Author(s):  
Martin Pienkowski ◽  
Jos J. Eggermont
Keyword(s):  
Auditory Cortex ◽  
Field Potential ◽  
Primary Auditory Cortex ◽  
Sound Level ◽  
Sound Frequency ◽  
Frequency Resolution ◽  
Neural Population ◽  
Complex Sounds ◽  
Individual Unit ◽  
Nonlinear Processing

The distribution of neuronal characteristic frequencies over the area of primary auditory cortex (AI) roughly reflects the tonotopic organization of the cochlea. However, because the area of AI activated by any given sound frequency increases erratically with sound level, it has generally been proposed that frequency is represented in AI not with a rate-place code but with some more complex, distributed code. Here, on the basis of both spike and local field potential (LFP) recordings in the anesthetized cat, we show that the tonotopic representation in AI is much more level tolerant when mapped with spectrotemporally dense tone pip ensembles rather than with individually presented tone pips. That is, we show that the tuning properties of individual unit and LFP responses are less variable with sound level under dense compared with sparse stimulation, and that the spatial frequency resolution achieved by the AI neural population at moderate stimulus levels (65 dB SPL) is better with densely than with sparsely presented sounds. This implies that nonlinear processing in the central auditory system can compensate (in part) for the level-dependent coding of sound frequency in the cochlea, and suggests that there may be a functional role for the cortical tonotopic map in the representation of complex sounds.

Difficulty of Singularity in Population Coding

2005 ◽  
Vol 17 (4) ◽  
pp. 839-858 ◽  
Author(s):  
Shun-ichi Amari ◽  
Hiroyuki Nakahara
Keyword(s):  
Fisher Information ◽  
Regularity Condition ◽  
Information Matrix ◽  
Population Coding ◽  
Neural Population ◽  
Binding Problem ◽  
Synchronous Firing ◽  
Novel Method ◽  
Pathological Behavior ◽  
Pathological Case

Fisher information has been used to analyze the accuracy of neural population coding. This works well when the Fisher information does not degenerate, but when two stimuli are presented to a population of neurons, a singular structure emerges by their mutual interactions. In this case, the Fisher information matrix degenerates, and the regularity condition ensuring the Cramér-Rao paradigm of statistics is violated. An animal shows pathological behavior in such a situation. We present a novel method of statistical analysis to understand information in population coding in which algebraic singularity plays a major role. The method elucidates the nature of the pathological case by calculating the Fisher information. We then suggest that synchronous firing can resolve singularity and show a method of analyzing the binding problem in terms of the Fisher information. Our method integrates a variety of disciplines in population coding, such as nonregular statistics, Bayesian statistics, singularity in algebraic geometry, and synchronous firing, under the theme of Fisher information.

scholarly journals Neural population encoding and decoding of sound source location across sound level in the rabbit inferior colliculus

2016 ◽  
Vol 115 (1) ◽  
pp. 193-207 ◽  
Author(s):  
Mitchell L. Day ◽  
Bertrand Delgutte
Keyword(s):  
Inferior Colliculus ◽  
Sound Source ◽  
Auditory Pathway ◽  
Relevant Information ◽  
Sound Level ◽  
Neural Population ◽  
Linear Transformations ◽  
Tuning Curves ◽  
Azimuthal Position ◽  
Sound Levels

At lower levels of sensory processing, the representation of a stimulus feature in the response of a neural population can vary in complex ways across different stimulus intensities, potentially changing the amount of feature-relevant information in the response. How higher-level neural circuits could implement feature decoding computations that compensate for these intensity-dependent variations remains unclear. Here we focused on neurons in the inferior colliculus (IC) of unanesthetized rabbits, whose firing rates are sensitive to both the azimuthal position of a sound source and its sound level. We found that the azimuth tuning curves of an IC neuron at different sound levels tend to be linear transformations of each other. These transformations could either increase or decrease the mutual information between source azimuth and spike count with increasing level for individual neurons, yet population azimuthal information remained constant across the absolute sound levels tested (35, 50, and 65 dB SPL), as inferred from the performance of a maximum-likelihood neural population decoder. We harnessed evidence of level-dependent linear transformations to reduce the number of free parameters in the creation of an accurate cross-level population decoder of azimuth. Interestingly, this decoder predicts monotonic azimuth tuning curves, broadly sensitive to contralateral azimuths, in neurons at higher levels in the auditory pathway.

scholarly journals When does recurrent connectivity improve neural population coding?

2014 ◽  
Vol 15 (S1) ◽  
Author(s):  
Joel Zylberberg ◽  
Eric Shea-Brown
Keyword(s):  
Population Coding ◽  
Neural Population

scholarly journals A signature of neural coding at human perceptual limits

2016 ◽  
Author(s):  
Paul M Bays
Keyword(s):  
Neural Coding ◽  
Empirical Relationship ◽  
Human Perception ◽  
Population Coding ◽  
Neural Population ◽  
Perceptual Awareness ◽  
Neural Basis ◽  
Visual Neurons ◽  
Error Magnitude ◽  
Subjective Confidence

AbstractSimple visual features, such as orientation, are thought to be represented in the spiking of visual neurons using population codes. I show that optimal decoding of such activity predicts characteristic deviations from the normal distribution of errors at low gains. Examining human perception of orientation stimuli, I show that these predicted deviations are present at near-threshold levels of contrast. The findings may provide a neural-level explanation for the appearance of a threshold in perceptual awareness, whereby stimuli are categorized as seen or unseen. As well as varying in error magnitude, perceptual judgments differ in certainty about what was observed. I demonstrate that variations in the total spiking activity of a neural population can account for the empirical relationship between subjective confidence and precision. These results establish population coding and decoding as the neural basis of perception and perceptual confidence.

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