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Articles, Behavioral/Systems/Cognitive

Nonlinear Spectrotemporal Interactions Underlying Selectivity for Complex Sounds in Auditory Cortex

Srivatsun Sadagopan and Xiaoqin Wang
Journal of Neuroscience 9 September 2009, 29 (36) 11192-11202; https://doi.org/10.1523/JNEUROSCI.1286-09.2009
Srivatsun Sadagopan
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Xiaoqin Wang
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    Figure 1.

    Selectivity for complex features in A1. A, Raster of example neuron's responses to marmoset vocalizations (n, “natural”; r, “reversed”). Gray shading corresponds to stimulus duration; different vocalization tokens had different lengths. Gray and black dots correspond to spontaneous spikes and spikes falling within our analysis window (15 ms after stimulus onset to 50 ms after stimulus offset) respectively. B, Responses to a particular token (“trill-twitter” call and reversed version) showed preference for natural over reversed call. Also note that maximal response occurs immediately following the initial upward-going trill segment. C, This unit responded strongly to a specific combination of two tone pips—a 5.8 kHz pip followed 75 ms later by a 6.9 kHz BF pip (red disk, both pips at 20 dB SPL). Purely second-order interaction map is plotted; image is smoothed for display. Colormap indicates the percentage of facilitation over sum of first-order responses (see Materials and Methods), and dark red contour and pink contour denote significance at p < 0.01 and p < 0.05 (modified permutation test) respectively. The nonlinear component was 180% of the sum of linear components (number in corner of interaction map). Gray lines are diagrams of lFM sweep stimuli tested in D; intensity corresponds to response strength (lightest = 0 spikes/s, darkest = 12.5 spikes/s; u, upward; d, downward lFM sweep direction). D, This unit strongly responded to “up” FM sweeps that connected the RF subunits in the nonlinear map and not to “down” FM sweeps that spanned the same frequency range (mean ± 1 SD plotted). The unit was tuned to an 80-ms-long upward lFM sweep spanning 5.6 kHz to 7.2 kHz (darkest gray line in C), precisely connecting the subunits. E, This unit was unresponsive to pure tones over a wide range of frequencies (2 octaves) and levels around estimated BF and BL (raster shown; frequency and level are interleaved on y-axis).

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    Figure 2.

    Example A1 neuron that displayed selectivity to repetitive stimuli. A, Subunits were observed at 8.5 kHz (BF) spaced at ∼50 ms intervals. Another significant subunit was located 0.25 octaves below BF occurring ∼25 ms earlier. B, Spike rasters of this neuron's response to Gaussian pulse trains (left) and lFM sweep trains (right) (5 sweeps per train). Responses were sustained throughout stimulus duration. C, Consistent with the nonlinear response map, this unit was tuned to interclick intervals of 40 and 80 ms when tested with Gaussian pulse train stimuli at BF (carrier frequency 8.5 kHz, click width SD 4 ms; mean ± 1 SD plotted). ICI, Interclick interval. D, The unit preferred 50-ms-long upward lFM sweeps trains ∼8.5 kHz with a preferred intersweep interval of 50 ms, also consistent with A. At least two sweeps were required to elicit a response. Darkest line corresponds to 7 sweeps in train, lightest line to a single sweep. ISI, Intersweep interval. Colormap, contours and raster conventions as in Figure 1. “u” and “d” refer to upward and downward lFM sweeps and are alternated along the axis.

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    Figure 3.

    Example A1 neuron showing purely spectral combination sensitivity. A, Nonlinear interaction map for this neuron showed excitatory subunits located 1/8 oct. above and below BF at coincident onset times with the BF pip. B, Spike rasters of this neuron's response to BPN of different BWs (left) and sAM at different modulation rates (AM rate; right). C, This unit was tuned to a bandwidth of ∼0.4 oct. centered at BF and BL, consistent with the nonlinear map obtained. D, Interestingly, this unit was narrowly tuned to sAM tones at 5.2 kHz modulated at 512 Hz. It should be noted that at a BF of 5.2 kHz, this amplitude modulation at 512 Hz produces spectral sidebands that are ∼1/8 oct. away from BF. Thus, these responses are also consistent with the nonlinear map in A. Error bars correspond to ± 1 SEM; dashed orange line is spontaneous rate. Colormap, Contours and raster conventions as in Figure 1.

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    Figure 4.

    Nonlinear spectrotemporal interactions underlie complex feature selectivity in A1. A, Nonlinear interaction map of another example A1 neuron that showed strong nonlinear interactions around a BF of 6.5 kHz. B, However, this unit did not respond to a wide variety of commonly used stimuli. Red circles are responses to two-pip stimuli and pink circles are responses to pure tones. Each dot is driven response rate (after subtracting spontaneous rate) to an individual stimulus belonging to that particular stimulus set. Abbreviations used in addition to those defined in text are as follows: FRA, frequency response area (tones); Col., colony noise (environmental sounds from monkey colony); Voc., marmoset vocalizations, BW − BPN of varying bandwidths. C, Raster of two-pip responses corresponding to map in A showing robust spiking occurred after integration of both pips. Colormap, Contours and raster conventions as in Figure 1.

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    Figure 5.

    Response enhancement by two-pip stimuli in nonlinear A1 neurons. A, Nonlinear neurons were not responsive to tones (median = 0 spikes/s, gray histogram). However, using two-pip stimuli, we could drive these neurons to a median of 24 spikes/s (black histogram), comparable to the median pure tone response (at BF) of tone-responsive neurons in the awake marmoset. (**p < 0.01; Wilcoxon rank-sum test). B, When we quantified degree of nonlinearity (see Materials and Methods), we observed a median 250% facilitation over the sum of linear components. C, Histograms of precision in frequency and time of the second-order subunits (half-maximal extent). Median spectral and temporal precision were 0.125 oct. and 12.5 ms respectively.

  • Figure 6.
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    Figure 6.

    Location and properties of nonlinear neurons in A1. A, Distribution of absolute depth (from dural surface) and relative depth (from first neuron encountered) of nonlinear (black dots) and tone-responsive (gray dots) units. Nonlinear units were located at shallower cortical depths compared with tone-responsive units (histograms of absolute and relative depths are on margins, numbers are medians; *p < 0.05, **p < 0.01, Wilcoxon rank-sum test). This suggested that nonlinear neurons may be localized to superficial cortical layers. B, Distribution of all observed subunits plotted relative to normalized BF. Gray ellipses are locations of statistically significant nonlinear subunits; shading corresponds to the percentage of facilitation. Most subunits were located <0.5 octaves away from BF suggesting a local computation. Most subunits also occurred within 50 ms of the BF pip.

  • Figure 7.
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    Figure 7.

    Generality and robustness of A1 nonlinear neurons. A, We did not find any differences in the distributions of BFs (top) or BLs (bottom) between nonlinear neurons (black histograms) and tone-responsive neurons (gray histograms) suggesting that the observed phenomenon was a general computation (n.s., not significant). B, High nonlinearity observed was not a consequence of using short stimuli to obtain linear response rates. When responses to 100-ms-long pure tones were used to compute single-tone response rates, we still observed significant nonlinear facilitation when compared with two-pip responses computed using 20–40 ms pips. The neuron falling below the diagonal (y = x line) presumably exhibited a stimulus length effect. C, Predicted versus actual preferred lFM velocities of a subset of nonlinear units (n = 22; **p < 0.01). Inset, Predicted versus actual preferred BWs of BPN stimuli in another subset of nonlinear units (n = 8; not significant). D, Distribution of actual p values from all neurons that met our initial screening criterion of peak p < 0.05, modified permutation test (n = 41, gray histogram). Dotted line indicates the Bonferroni-corrected p value criterion for the most common stimulus set (n = 289 stimuli). Thirty-nine of 41 neurons met this criterion.

  • Figure 8.
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    Figure 8.

    Differential representation of vocalizations in pure-tone and nonlinear neuron populations. A, Spike raster of an example pure-tone neuron responding to 20 natural marmoset vocalizations (n) and their reversed versions (r). Gray shading is stimulus duration, black dots are spikes falling within analysis window, gray dots are spontaneous spikes. B, Same as A, but for an example nonlinear neuron. C, Distribution of firing rates obtained from the raster in A for the pure-tone neuron. D, Same as C, but for nonlinear neurons. κ denotes the excess kurtosis of the distributions. E, Distributions of selectivity for nonlinear (black) and pure-tone (gray) neurons. Nonlinear neurons exhibited more selective responses than pure-tone neurons (*p < 0.05, Kolmogorov–Smirnov test). F, Distributions of population sparseness for nonlinear (black) and pure-tone (gray) neurons. Nonlinear neurons represented complex stimuli more sparsely than pure-tone neurons (**p < 0.01, Kolmogorov–Smirnov test).

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The Journal of Neuroscience: 29 (36)
Journal of Neuroscience
Vol. 29, Issue 36
9 Sep 2009
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Nonlinear Spectrotemporal Interactions Underlying Selectivity for Complex Sounds in Auditory Cortex
Srivatsun Sadagopan, Xiaoqin Wang
Journal of Neuroscience 9 September 2009, 29 (36) 11192-11202; DOI: 10.1523/JNEUROSCI.1286-09.2009

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Nonlinear Spectrotemporal Interactions Underlying Selectivity for Complex Sounds in Auditory Cortex
Srivatsun Sadagopan, Xiaoqin Wang
Journal of Neuroscience 9 September 2009, 29 (36) 11192-11202; DOI: 10.1523/JNEUROSCI.1286-09.2009
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  • Combination sensitivity: not (just) a cortical thing
    Jeffrey J Wenstrup
    Published on: 16 October 2009
  • Published on: (16 October 2009)
    Page navigation anchor for Combination sensitivity: not (just) a cortical thing
    Combination sensitivity: not (just) a cortical thing
    • Jeffrey J Wenstrup, Professor and Chair

    Sadagopan and Wang demonstrate that neurons in marmoset auditory cortex display combination sensitivity that underlies responsiveness to complex sounds. This work reinforces evidence of continuity in processing of complex sounds across a range of mammals and other vertebrates. My comments relate to the article’s proposal that combination sensitivity in marmoset cortex is likely the result of interactions that occur in...

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    Sadagopan and Wang demonstrate that neurons in marmoset auditory cortex display combination sensitivity that underlies responsiveness to complex sounds. This work reinforces evidence of continuity in processing of complex sounds across a range of mammals and other vertebrates. My comments relate to the article’s proposal that combination sensitivity in marmoset cortex is likely the result of interactions that occur in auditory cortex, especially in its upper layers. In bats and mice, there is now clear evidence that combination-sensitive responses originate at levels below auditory cortex. The issue is particularly relevant to our understanding of the mechanisms that create such interesting response properties.

    A wide diversity of combination-sensitive responses occurs in the inferior colliculus (IC) of bats and other mammals, characterized by facilitated responses to tone or noise combinations. The frequency combinations can be separated by a few kilohertz or several octaves. The temporal sensitivity can be on a sub-millisecond scale or on a scale of tens of milliseconds, and often does not depend on the duration of the facilitating sounds. Thus, in each of these features, combination sensitivity observed in the marmoset cortex is similar to that observed in the IC of bats and mice (Mittmann and Wenstrup, 1995; Yan and Suga, 1996; Portfors and Wenstrup, 1999; Leroy and Wenstrup, 2000; Nataraj and Wenstrup, 2005; Portfors and Felix, 2005; Gans et al., 2009).

    Concerning mechanisms, work in the mustached bat shows that the interactions underlying combination-sensitive facilitation arise through differently tuned glycinergic inputs onto IC neurons. Interestingly, glutamatergic inputs do not appear to play a role in the facilitation, even though they mediate responses to single tonal stimuli in the same neurons (Wenstrup and Leroy, 2001; Nataraj and Wenstrup, 2005; Sanchez et al., 2008).

    While this article proposes that combination sensitivity arises via cortical processing, the available evidence suggests that such interactions likely originate in the IC. Things may be different in the marmoset, but, given the similarity to response features in other mammals, there is no a priori reason to make that assumption. Instead, I suggest that the combination sensitivity originating in the IC becomes more apparent as subsequent processing suppresses the responses to single tonal stimuli.

    REFERENCES

    Gans D, Sheykholeslami K, Peterson D, Wenstrup JJ (2009) Temporal features of spectral integration in the inferior colliculus: effects of stimulus duration and rise time. J Neurophysiol 102:167-180.

    Leroy SA, Wenstrup JJ (2000) Spectral integration in the inferior colliculus of the mustached bat. J Neurosci 20:8533-8541.

    Mittmann DH, Wenstrup JJ (1995) Combination-sensitive neurons in the inferior colliculus. Hear Res 90:185-191.

    Nataraj K, Wenstrup JJ (2005) Roles of inhibition in creating complex auditory responses in the inferior colliculus: facilitated combination-sensitive neurons. J Neurophysiol 93:3294-3312.

    Portfors CV, Felix RA 2nd (2005) Spectral integration in the inferior colliculus of the CBA/CaJ mouse. Neurosci 136:1159-1170.

    Portfors CV, Wenstrup JJ (1999) Delay-tuned neurons in the inferior colliculus of the mustached bat: implications for analyses of target distance. J Neurophysiol 82:1326-1338.

    Sanchez JT, Gans D, Wenstrup JJ (2008) Glycinergic “inhibition” mediates selective excitatory responses to combinations of sounds. J Neurosci 28:80 - 90.

    Wenstrup J, Leroy SA (2001) Spectral integration in the inferior colliculus: role of glycinergic inhibition in response facilitation. J Neurosci 21:RC124.

    Yan J, Suga N (1996) The midbrain creates and the thalamus sharpens echo- delay tuning for the cortical representation of target-distance information in the mustached bat. Hear Res 93: 102-110.

    Show Less
    Competing Interests: None declared.

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