1887
banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
oa
Effect of auditory-nerve response variability on estimates of tuning curves
Rent:
Rent this article for
Access full text Article
/content/asa/journal/jasa/122/6/10.1121/1.2794880
1.
1.Bruce, I. C. , Sachs, M. B. , and Young, E. D. (2003). “An auditory-periphery model of the effects of acoustic trauma on auditory nerve responses,” J. Acoust. Soc. Am. 113, 369388.
http://dx.doi.org/10.1121/1.1519544
2.
2.Carney, L. H. , Heinz, M. G. , Evilsizer, M. E. , Gilkey, R. H. , and Colburn, H. S. (2002). “Auditory phase opponency: A temporal model for masked detection at low frequencies,” Acust. Acta Acust. 88, 334347.
3.
3.Cedolin, L. , and Delgutte, B. (2007). “Spatio-temporal representation of the pitch of complex tones in the auditory nerve,” in Hearing–From Sensory Processing to Perception, edited by B. Kollmeier, G. Klump, V. Hohmann, U. Langemann, M. Mauermann, S. Uppenkamp, and J. Verhey (Springer, Berlin), pp. 6170.
4.
4.Deng, L. , and Geisler, C. D. (1987). “A composite auditory model for processing speech sounds,” J. Acoust. Soc. Am. 82, 20012012.
http://dx.doi.org/10.1121/1.395644
5.
5.Evans, E. F. (1972). “The frequency response and other properties of single fibres in the guinea-pig cochlear nerve,” J. Physiol. 226, 263287.
6.
6.Heil, P. , Neubauer, H. , Irvine, D. R. , and Brown, M. (2007). “Spontaneous activity of auditory-nerve fibers: Insights into stochastic processes at ribbon synapses,” J. Neurosci. 27, 84578474.
7.
7.Heinz, M. G. (2007). “Spatiotemporal encoding of vowels in noise studied with the responses of individual auditory nerve fibers,” in Hearing–From Sensory Processing to Perception, edited by B. Kollmeier, G. Klump, V. Hohmann, U. Langemann, M. Mauermann, S. Uppenkamp, and J. Verhey (Springer-Verlag, Berlin), pp. 107115.
8.
8.Heinz, M. G. , Colburn, H. S. , and Carney, L. H. (2001). “Rate and timing cues associated with the cochlear amplifier: Level discrimination based on monaural cross-frequency coincidence detection,” J. Acoust. Soc. Am. 110, 20652084.
http://dx.doi.org/10.1121/1.1404977
9.
9.Heinz, M. G. , and Young, E. D. (2004). “Response growth with sound level in auditory-nerve fibers after noise-induced hearing loss,” J. Neurophysiol. 91, 784795.
10.
10.Joris, P. X. , Van de Sande, B. , Louage, D. H. , and van der Heijden, M. (2006). “Binaural and cochlear disparities,” Proc. Natl. Acad. Sci. U.S.A. 103, 1291712922.
11.
11.Kiang, N. Y. S. , Watanabe, T. , Thomas, E. C. , and Clark, L. F. (1965). Discharge Patterns of Single Fibers in the Cat’s Auditory Nerve (MIT Press, Cambridge, MA).
12.
12.Liberman, M. C. (1978). “Auditory-nerve response from cats raised in a low-noise chamber,” J. Acoust. Soc. Am. 63, 442455.
http://dx.doi.org/10.1121/1.381736
13.
13.Miller, R. L. , Schilling, J. R. , Franck, K. R. , and Young, E. D. (1997). “Effects of acoustic trauma on the representation of the vowel /ε/ in cat auditory nerve fibers,” J. Acoust. Soc. Am. 101, 36023616.
http://dx.doi.org/10.1121/1.418321
14.
14.Palmer, A. R. (1990). “The representation of the spectra and fundamental frequencies of steady-state single- and double-vowel sounds in the temporal discharge patterns of guinea pig cochlear-nerve fibers,” J. Acoust. Soc. Am. 88, 14121426.
http://dx.doi.org/10.1121/1.400329
15.
15.Sachs, M. B. , and Young, E. D. (1979). “Encoding of steady-state vowels in the auditory nerve: Representation in terms of discharge rate,” J. Acoust. Soc. Am. 66, 470479.
http://dx.doi.org/10.1121/1.383098
16.
16.Shamma, S. A. (1985). “Speech processing in the auditory system. I: The representation of speech sounds in the responses of the auditory nerve,” J. Acoust. Soc. Am. 78, 16121621.
http://dx.doi.org/10.1121/1.392799
17.
17.Winter, I. M. , and Palmer, A. R. (1991). “Intensity coding in low-frequency auditory-nerve fibers of the guinea pig,” J. Acoust. Soc. Am. 90, 19581967.
http://dx.doi.org/10.1121/1.401675
18.
18.Young, E. D. , and Barta, P. E. (1986). “Rate responses of auditory nerve fibers to tones in noise near masked threshold,” J. Acoust. Soc. Am. 79, 426442.
http://dx.doi.org/10.1121/1.393530
19.
19.Young, E. D. , and Sachs, M. B. (1979). “Representation of steady-state vowels in the temporal aspects of the discharge patterns of populations of auditory-nerve fibers,” J. Acoust. Soc. Am. 66, 13811403.
http://dx.doi.org/10.1121/1.383532
20.
20.Zhang, X. , Heinz, M. G. , Bruce, I. C. , and Carney, L. H. (2001). “A phenomenological model for the responses of auditory-nerve fibers: I. Nonlinear tuning with compression and suppression,” J. Acoust. Soc. Am. 109, 648670.
http://dx.doi.org/10.1121/1.1336503
21.
21.Zilany, M. S. A. , and Bruce, I. C. (2006). “Modeling auditory-nerve responses for high sound pressure levels in the normal and impaired auditory periphery,” J. Acoust. Soc. Am. 120, 14461466.
http://dx.doi.org/10.1121/1.2225512
http://aip.metastore.ingenta.com/content/asa/journal/jasa/122/6/10.1121/1.2794880
Loading
View: Figures

Figures

Image of FIG. 1.

Click to view

FIG. 1.

(Color online) Tuning curves measured by the adaptive algorithm with and without randomness for an AN-model fiber with and . (A) AN model without randomness. Individual data points represent the thresholds estimated by the adaptive algorithm at each frequency. Solid lines represent a smoothed curve (see the text). Identical tuning curves were obtained with repeated measures. (B) AN model with randomness. (C) Five repeated tuning curves with randomness. Derived parameters for the individual repetitions are: CF ; geometric ; and octaves. Threshold ; ; . ; geo. ; octaves. The information may not be properly conveyed in black and white.

Image of FIG. 2.

Click to view

FIG. 2.

(Color online) (A) Populations of thresholds, (B) values, and (C) CF errors estimated from tuning curves measured for 250 model AN fibers. (A and B) Each symbol (×) represents the estimated value with randomness and red lines represent nonrandom estimates. (B) Black dashed lines represent the range between the 5th and 95th percentile of experimental values within each CF region (Miller et al., 1997). (C) Error in estimated CFs from tuning curves measured when randomness was included. CF error in octaves is computed as (estimated CF/model CF). The information in (A) and (B) may not be properly conveyed in black and white.

Image of FIG. 3.

Click to view

FIG. 3.

(Color online) Repeated tuning curves measured from three AN fibers from chinchillas, which vary in SR and CF. Derived parameters for the individual repetitions are: (A: ) CF ; geometric ; octaves. Threshold ; ; . ; geo. ; octs. (B: ) ; geo. ; octs. ; ; . ; geo. ; octs. (C: ) ; geo. ; octs. ; ; . ; geo. ; octs. The information may not be properly conveyed in black and white.

Loading

Article metrics loading...

/content/asa/journal/jasa/122/6/10.1121/1.2794880
2007-10-26
2014-04-20

Abstract

Near-Poisson variability in auditory-nerve (AN) responses limits the accuracy of automated tuning-curve algorithms. Here, a typical adaptive tuning-curve algorithm was used with a physiologically realistic AN model with and without the inclusion of neural randomness. Response randomness produced variability in estimates that was nearly as large as in AN data. Results suggest that it is sufficient for AN models to specify frequency selectivity based on mean values at each characteristic frequency (CF). Errors in estimates of CF, which decreased from octaves at low frequencies to octaves at high frequencies, are significant for studies of spatiotemporal coding.

Loading

Full text loading...

/deliver/fulltext/asa/journal/jasa/122/6/1.2794880.html;jsessionid=aignkw6vhdm1.x-aip-live-03?itemId=/content/asa/journal/jasa/122/6/10.1121/1.2794880&mimeType=html&fmt=ahah&containerItemId=content/asa/journal/jasa
true
true
This is a required field
Please enter a valid email address
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Effect of auditory-nerve response variability on estimates of tuning curves
http://aip.metastore.ingenta.com/content/asa/journal/jasa/122/6/10.1121/1.2794880
10.1121/1.2794880
SEARCH_EXPAND_ITEM