The effect of species-specific vocalization on the discharge of auditory cortical cells in the awake squirrel monkey (Saimiri sciureus)

Winter, Peter F.; Funkenstein, H. Harris

doi:10.1007/bf00234133

Cited by 146 publications

(53 citation statements)

References 17 publications

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“…Lesion studies indicate that the primary auditory cortex (A1) is essential for the recognition of communication sounds (Heffner and Heffner, 1986). Early studies into the representation of vocalization sounds were optimistic about finding specific "call detectors" (Winter and Funkenstein, 1973) within the auditory cortex, but, although many neurons in A1 respond vigorously to species-specific vocalizations, previous research suggests that these responses may often be explained in terms of sensitivity to relatively simple acoustic features of the sounds (Pelleg-Toiba and Wollberg, 1991;Gehr et al, 2000). Nevertheless, a recent study by Wang et al (1995), which compared responses of neurons in A1 of the common marmoset (Callithrix jacchus) to natural and time-reversed marmoset "twitter" vocalizations, found that marmoset A1 neurons often responded much more vigorously to natural than to time-reversed vocalizations.…”

Section: Introductionmentioning

confidence: 99%

Plasticity of Temporal Pattern Codes for Vocalization Stimuli in Primary Auditory Cortex

Schnupp

Hall²,

Kokelaar³

et al. 2006

J. Neurosci.

204

221

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It has been suggested that "call-selective" neurons may play an important role in the encoding of vocalizations in primary auditory cortex (A1). For example, marmoset A1 neurons often respond more vigorously to natural than to time-reversed twitter calls, although the spectral energy distribution in the natural and time-reversed signals is the same. Neurons recorded in cat A1, in contrast, showed no such selectivity for natural marmoset calls. To investigate whether call selectivity in A1 can arise purely as a result of auditory experience, we recorded responses to marmoset calls in A1 of naive ferrets, as well as in ferrets that had been trained to recognize these natural marmoset calls. We found that training did not induce call selectivity for the trained vocalizations in A1. However, although ferret A1 neurons were not call selective, they efficiently represented the vocalizations through temporal pattern codes, and trained animals recognized marmoset twitters with a high degree of accuracy. These temporal patterns needed to be analyzed at timescales of 10 -50 ms to ensure efficient decoding. Training led to a substantial increase in the amount of information transmitted by these temporal discharge patterns, but the fundamental nature of the temporal pattern code remained unaltered. These results emphasize the importance of temporal discharge patterns and cast doubt on the functional significance of call-selective neurons in the processing of animal communication sounds at the level of A1.

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Section: Introductionmentioning

confidence: 99%

Plasticity of Temporal Pattern Codes for Vocalization Stimuli in Primary Auditory Cortex

Schnupp

Hall²,

Kokelaar³

et al. 2006

J. Neurosci.

204

221

View full text Add to dashboard Cite

show abstract

“…Early studies as well as more recent investigations of the neural representation of species-specific vocal communication sounds in primates and several other species have typically involved playing individual vocalization exemplars or "tokens" and recording the elicited neural responses (Cohen et al 2004;Newman and Wollberg 1973;Rauschecker et al 1995;Goldman-Rakic 2002, 2005;Tian et al 2001;Wang et al 1995;Winter and Funkenstein 1973;Wollberg and Newman 1972). Although this approach based on token vocalizations has provided useful insights, it cannot fully elucidate the neural representations of species-specific vocalizations for two important reasons.…”

Section: Introductionmentioning

confidence: 99%

Virtual Vocalization Stimuli for Investigating Neural Representations of Species-Specific Vocalizations

DiMattina

Wang²

2006

Journal of Neurophysiology

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. Most studies investigating neural representations of species-specific vocalizations in nonhuman primates and other species have involved studying neural responses to vocalization tokens. One limitation of such approaches is the difficulty in determining which acoustical features of vocalizations evoke neural responses. Traditionally used filtering techniques are often inadequate in manipulating features of complex vocalizations. Furthermore, the use of vocalization tokens cannot fully account for intrinsic stochastic variations of vocalizations that are crucial in understanding the neural codes for categorizing and discriminating vocalizations differing along multiple feature dimensions. In this work, we have taken a rigorous and novel approach to the study of species-specific vocalization processing by creating parametric "virtual vocalization" models of major call types produced by the common marmoset (Callithrix jacchus). The main findings are as follows. 1) Acoustical parameters were measured from a database of the four major call types of the common marmoset. This database was obtained from eight different individuals, and for each individual, we typically obtained hundreds of samples of each major call type. 2) These feature measurements were employed to parameterize models defining representative virtual vocalizations of each call type for each of the eight animals as well as an overall species-representative virtual vocalization averaged across individuals for each call type. 3) Using the same feature-measurement that was applied to the vocalization samples, we measured acoustical features of the virtual vocalizations, including features not explicitly modeled and found the virtual vocalizations to be statistically representative of the callers and call types. 4) The accuracy of the virtual vocalizations was further confirmed by comparing neural responses to real and synthetic virtual vocalizations recorded from awake marmoset auditory cortex. We found a strong agreement between the responses to token vocalizations and their synthetic counterparts. 5) We demonstrated how these virtual vocalization stimuli could be employed to precisely and quantitatively define the notion of vocalization "selectivity" by using stimuli with parameter values both within and outside the naturally occurring ranges. We also showed the potential of the virtual vocalization stimuli in studying issues related to vocalization categorizations by morphing between different call types and individual callers.

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“…In other mammals, such an approach is made more difficult by their less specialized behavioral repertoire and a bewildering number of natural complex sounds that may be expected to trigger responses in nonprimary cortical neurons. One group of species-specific sounds that has been used with some success in nonhuman primates in the past (Newman and Symmes 1974;Symmes 1981;Winter and Funkenstein 1973;Wollberg and Newman 1973) and again more recently (Eliades and Wang 2003;Rauschecker and Tian 2000;Rauschecker et al 1995;Tian et al 2001;Wang 2000;Wang and Kadia 2001;Wang et al 1995) is that of vocalizations. Although their spectra are known, however, they are still too complex to be used as a first-or second-order stimulus with any hope of finding stimulus-specific characteristics across different cortical areas.…”

Section: Introductionmentioning

confidence: 99%

Processing of Frequency-Modulated Sounds in the Lateral Auditory Belt Cortex of the Rhesus Monkey

Tian

Rauschecker

2004

Journal of Neurophysiology

153

108

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Tian, Biao and Josef P. Rauschecker. Processing of frequencymodulated sounds in the lateral auditory belt cortex of the rhesus monkey. J Neurophysiol 92: 2993-3013, 2004; 10.1152/ jn.00472.2003. Single neurons were recorded from the lateral belt areas, anterolateral (AL), mediolateral (ML), and caudolateral (CL), of nonprimary auditory cortex in 4 adult rhesus monkeys under gas anesthesia, while the neurons were stimulated with frequency-modulated (FM) sweeps. Responses to FM sweeps, measured as the firing rate of the neurons, were invariably greater than those to tone bursts. In our stimuli, frequency changed linearly from low to high frequencies (FM direction "up") or high to low frequencies ("down") at varying speeds (FM rates). Neurons were highly selective to the rate and direction of the FM sweep. Significant differences were found between the 3 lateral belt areas with regard to their FM rate preferences: whereas neurons in ML responded to the whole range of FM rates, AL neurons responded better to slower FM rates in the range of naturally occurring communication sounds. CL neurons generally responded best to fast FM rates at a speed of several hundred Hz/ms, which have the broadest frequency spectrum. These selectivities are consistent with a role of AL in the decoding of communication sounds and of CL in the localization of sounds, which works best with broader bandwidths. Together, the results support the hypothesis of parallel streams for the processing of different aspects of sounds, including auditory objects and auditory space.

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The effect of species-specific vocalization on the discharge of auditory cortical cells in the awake squirrel monkey (Saimiri sciureus)

Cited by 146 publications

References 17 publications

Plasticity of Temporal Pattern Codes for Vocalization Stimuli in Primary Auditory Cortex

Plasticity of Temporal Pattern Codes for Vocalization Stimuli in Primary Auditory Cortex

Virtual Vocalization Stimuli for Investigating Neural Representations of Species-Specific Vocalizations

Processing of Frequency-Modulated Sounds in the Lateral Auditory Belt Cortex of the Rhesus Monkey

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