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. 2014 Jan 22;34(4):1306-13.
doi: 10.1523/JNEUROSCI.3031-13.2014.

Suboptimal use of neural information in a mammalian auditory system

Affiliations

Suboptimal use of neural information in a mammalian auditory system

Laurel H Carney et al. J Neurosci. .

Abstract

Establishing neural determinants of psychophysical performance requires both behavioral and neurophysiological metrics amenable to correlative analyses. It is often assumed that organisms use neural information optimally, such that any information available in a neural code that could improve behavioral performance is used. Studies have shown that detection of amplitude-modulated (AM) auditory tones by humans is correlated to neural synchrony thresholds, as recorded in rabbit at the level of the inferior colliculus, the first level of the ascending auditory pathway where neurons are tuned to AM stimuli. Behavioral thresholds in rabbit, however, are ∼10 dB higher (i.e., 3 times less sensitive) than in humans, and are better correlated to rate-based than temporal coding schemes in the auditory midbrain. The behavioral and physiological results shown here illustrate an unexpected, suboptimal utilization of available neural information that could provide new insights into the mechanisms that link neuronal function to behavior.

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Figures

Figure 1.
Figure 1.
Modulation transfer functions (i.e., sAM detection threshold vs Fm) for human and rabbit. Symbols (solid, human; open, rabbit) represent thresholds determined for individual listeners; solid lines are the mean thresholds for each species. More sensitive detection thresholds are at the top of each graph. a,Thresholds for sAM tones. Stimuli were sAM 5 kHz tones presented at 50 dB SPL. Downward arrows indicate conditions for which the animals could not perform at criterion; mean thresholds do not include these values. b, Thresholds for sAM wideband noise presented at 20 dB SPL spectrum level; noise bandwidth was 0.1–10 kHz. All thresholds were based on a single-interval choice task with 500 ms duration stimuli. For all frequencies tested humans almost invariably had higher sensitivity to AM than rabbits.
Figure 2.
Figure 2.
Response properties of two representative neurons in the IC of awake rabbit. a, Response maps. Audio frequency tuning is described as changes in average discharge rate as a function of tone frequency at several sound levels. The gray lines indicate spontaneous rate. b, MTFs based on discharge rate. Examples of bandpass (left) and band-reject (right) MTFs are shown. BMF is determined by the peak of bandpass MTFs or the minimum in band-reject MTFs. c, Average discharge rate (blue) and synchrony (red, sync coefficient) as a function of modulation depth. Neural thresholds for AM detection for each metric (arrows) were estimated from the responses to stimuli that varied in modulation depth. Responses to unmodulated (U) stimuli are shown by the open symbols at the left. Distributions of the average discharge rate for each modulation depth were analyzed using the ROC technique to determine neural rate thresholds for AM detection. Neural thresholds based on synchrony were computed using circular statistics applied to period histograms binned on the modulation frequency (see Materials and Methods). m, Modulation depth.
Figure 3.
Figure 3.
Neural thresholds as a function of modulation frequency. Neural thresholds are shown for responses to sAM tones (a) and sAM noise (b). Mean behavioral thresholds from Figure 1 are shown for human (black line) and rabbit (gray line; mean ± 1 SD, light gray regions). More sensitive thresholds are at the top of each graph. Left, Blue symbols indicate single neuron thresholds for AM detection based on changes in average discharge rate. Right, Red symbols indicate single neuron thresholds based on synchronization to the sAM stimulus envelope. Red squares at −30 dB (top right graph) indicate two thresholds that were below the lowest modulation depth tested. Note that average discharge rate thresholds could explain rabbit, but not human, detection thresholds, whereas synchronization thresholds often matched the human threshold values (see Materials and Methods).
Figure 4.
Figure 4.
Histograms of neural thresholds combined across modulation frequencies referenced to the mean rabbit behavioral threshold. Mean behavioral threshold for rabbit is shown as the vertical dashed line ±1 SD (gray). Thresholds based on average discharge rate are shown in blue; thresholds based on synchronization (sync) to the stimulus envelope are displayed in red. a, Distributions of sAM detection thresholds with respect to the mean behavioral threshold for 5 kHz sAM tones. b, Distributions of sAM detection thresholds with respect to mean behavioral threshold for sAM noise. For both sAM tone and sAM noise conditions, the most sensitive (right-most) rate thresholds were consistent with mean behavioral thresholds in rabbit, whereas sync thresholds were generally more sensitive than mean behavioral thresholds (i.e., the distribution of sync thresholds extends to the right side of the graph).

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References

    1. Attias H, Schreiner CE. Advances in neural information processing systems. Vol 9. Cambridge: MIT; 1997. Low-order temporal statistics of natural sounds; pp. 27–33.
    1. Barlow H. Redundancy reduction revisited. Network. 2001;12:241–253. doi: 10.1080/net.12.3.241.253. - DOI - PubMed
    1. Baumann S, Griffiths TD, Sun L, Petkov CI, Thiele A, Rees A. Orthogonal representation of sound dimensions in the primate midbrain. Nat Neurosci. 2011;14:423–425. doi: 10.1038/nn.2771. - DOI - PMC - PubMed
    1. Bendor D, Wang X. Neural coding of periodicity in marmoset auditory cortex. J Neurophysiol. 2010;103:1809–1822. doi: 10.1152/jn.00281.2009. - DOI - PMC - PubMed
    1. Borst A, Theunissen FE. Information theory and neural coding. Nat Neurosci. 1999;2:947–957. doi: 10.1038/14731. - DOI - PubMed

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