Literature DB >> 7928711

Topographic representation of tone intensity along the isofrequency axis of cat primary auditory cortex.

P Heil1, R Rajan, D R Irvine.   

Abstract

The sound pressure level (SPL), henceforth termed intensity, of acoustic signals is encoded in the central auditory system by neurons with different forms of intensity sensitivity. However, knowledge about the topographic organization of neurons with these different properties and hence about the spatial representation of intensity, especially at higher levels of the auditory pathway, is limited. Here we show that in the tonotopically organized primary auditory cortex (AI) of the cat there are orderly topographic organizations, along the isofrequency axis, of several neuronal properties related to the coding of the intensity of tones, viz. minimum threshold, dynamic range, best SPL, and non-monotonicity of spike count--intensity functions to tones of characteristic frequency (CF). Minimum threshold, dynamic range, and best SPL are correlated and alter periodically along isofrequency strips. The steepness of the high-intensity descending slope of spike count--intensity functions also varies systematically, with steepest slopes occurring in the regions along an isofrequency strip where low thresholds, narrow dynamic ranges and low best SPLs are found. As a consequence, CF-tones of various intensities are represented by orderly and, for most intensities, periodic, spatial patterns of distributed neuronal activity along an isofrequency strip. For low--to--moderate intensities, the mean relative activity along the entire isofrequency strip increases rapidly with intensity, with the spatial pattern of activity remaining quite constant along the strip. At higher intensities, however, the mean relative activity along the strip remains fairly constant with changes in intensity, but the spatial patterns change markedly. As a consequence of these effects, low- and high-intensity tones are represented by complementary distributions of activity alternating along an isofrequency strip. We conclude that in AI tone intensity is represented by two complementary modes, viz. discharge rate and place. Furthermore, the magnitude of the overall changes in the representation of tone intensity in AI appears to be closely related to psychophysical measures of loudness and of intensity discrimination.

Mesh:

Year:  1994        PMID: 7928711     DOI: 10.1016/0378-5955(94)90099-x

Source DB:  PubMed          Journal:  Hear Res        ISSN: 0378-5955            Impact factor:   3.208


  11 in total

1.  Modular organization of intrinsic connections associated with spectral tuning in cat auditory cortex.

Authors:  H L Read; J A Winer; C E Schreiner
Journal:  Proc Natl Acad Sci U S A       Date:  2001-07-03       Impact factor: 11.205

2.  Level-tuned neurons in primary auditory cortex adapt differently to loud versus soft sounds.

Authors:  Paul V Watkins; Dennis L Barbour
Journal:  Cereb Cortex       Date:  2010-05-10       Impact factor: 5.357

3.  Associative learning shapes the neural code for stimulus magnitude in primary auditory cortex.

Authors:  Daniel B Polley; Marc A Heiser; David T Blake; Christoph E Schreiner; Michael M Merzenich
Journal:  Proc Natl Acad Sci U S A       Date:  2004-11-08       Impact factor: 11.205

4.  Effects of sound level on fMRI activation in human brainstem, thalamic and cortical centers.

Authors:  Irina S Sigalovsky; Jennifer R Melcher
Journal:  Hear Res       Date:  2006-04-27       Impact factor: 3.208

5.  Nonmonotonic synaptic excitation and imbalanced inhibition underlying cortical intensity tuning.

Authors:  Guangying K Wu; Pingyang Li; Huizhong W Tao; Li I Zhang
Journal:  Neuron       Date:  2006-11-22       Impact factor: 17.173

6.  Remodeling the cortex in memory: Increased use of a learning strategy increases the representational area of relevant acoustic cues.

Authors:  Kasia M Bieszczad; Norman M Weinberger
Journal:  Neurobiol Learn Mem       Date:  2010-04-29       Impact factor: 2.877

7.  Decoding sound level in the marmoset primary auditory cortex.

Authors:  Wensheng Sun; Ellisha N Marongelli; Paul V Watkins; Dennis L Barbour
Journal:  J Neurophysiol       Date:  2017-07-12       Impact factor: 2.714

8.  Linking the response properties of cells in auditory cortex with network architecture: cotuning versus lateral inhibition.

Authors:  Jaime de la Rocha; Cristina Marchetti; Max Schiff; Alex D Reyes
Journal:  J Neurosci       Date:  2008-09-10       Impact factor: 6.167

Review 9.  Development and plasticity of intra- and intersensory information processing.

Authors:  Daniel B Polley; Andrea R Hillock; Christopher Spankovich; Maria V Popescu; David W Royal; Mark T Wallace
Journal:  J Am Acad Audiol       Date:  2008 Nov-Dec       Impact factor: 1.664

10.  Learning strategy trumps motivational level in determining learning-induced auditory cortical plasticity.

Authors:  Kasia M Bieszczad; Norman M Weinberger
Journal:  Neurobiol Learn Mem       Date:  2009-10-21       Impact factor: 2.877

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.