Hierarchical Auditory Processing Directed Rostrally along the Monkey's Supratemporal Plane

Kikuchi, Y.; Horwitz, Barry; Mishkin, Mortimer

doi:10.1523/jneurosci.2267-10.2010

Cited by 91 publications

(113 citation statements)

References 36 publications

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“…Some level of regional specificity is expected, given that, for example, relatively simple sounds are not a very effective drive for neurons in voice-sensitive cortex (13,14). However, we did not find strong evidence for any visual or auditory stimulus specificity in the degree of phase resetting or the proportions of multisensory responses.…”

Section: Discussioncontrasting

confidence: 77%

“…However, such misalignment can drastically affect neuronal responses in ways that may also differ between brain regions (8-10). We asked how natural asynchronies in the onset of face/voice content in communication signals would affect voice-sensitive cortex, a region in the ventral "object" pathway (11) where neurons (i) are selective for auditory features in communication sounds (12)(13)(14), (ii) are influenced by visual "face" content (12), and (iii) display relatively slow and temporally variable responses in comparison with neurons in primary auditory cortical or subcortical structures (14-16).Neurophysiological studies in human and nonhuman animals have provided considerable insights into the role of cortical oscillations during multisensory conditions and for parsing speech. Cortical oscillations entrain to the slow temporal dynamics of natural sounds (17)(18)(19)(20) and are thought to reflect the excitability of local networks to sensory inputs (21-24).…”

mentioning

confidence: 99%

“…However, such misalignment can drastically affect neuronal responses in ways that may also differ between brain regions (8)(9)(10). We asked how natural asynchronies in the onset of face/voice content in communication signals would affect voice-sensitive cortex, a region in the ventral "object" pathway (11) where neurons (i) are selective for auditory features in communication sounds (12)(13)(14), (ii) are influenced by visual "face" content (12), and (iii) display relatively slow and temporally variable responses in comparison with neurons in primary auditory cortical or subcortical structures (14)(15)(16).…”

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confidence: 99%

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Natural asynchronies in audiovisual communication signals regulate neuronal multisensory interactions in voice-sensitive cortex

Perrodin

Kayser

Logothetis

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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When social animals communicate, the onset of informative content in one modality varies considerably relative to the other, such as when visual orofacial movements precede a vocalization. These naturally occurring asynchronies do not disrupt intelligibility or perceptual coherence. However, they occur on time scales where they likely affect integrative neuronal activity in ways that have remained unclear, especially for hierarchically downstream regions in which neurons exhibit temporally imprecise but highly selective responses to communication signals. To address this, we exploited naturally occurring face-and voice-onset asynchronies in primate vocalizations. Using these as stimuli we recorded cortical oscillations and neuronal spiking responses from functional MRI (fMRI)-localized voice-sensitive cortex in the anterior temporal lobe of macaques. We show that the onset of the visual face stimulus resets the phase of low-frequency oscillations, and that the face-voice asynchrony affects the prominence of two key types of neuronal multisensory responses: enhancement or suppression. Our findings show a three-way association between temporal delays in audiovisual communication signals, phase-resetting of ongoing oscillations, and the sign of multisensory responses. The results reveal how natural onset asynchronies in cross-sensory inputs regulate network oscillations and neuronal excitability in the voice-sensitive cortex of macaques, a suggested animal model for human voice areas. These findings also advance predictions on the impact of multisensory input on neuronal processes in face areas and other brain regions.H ow the brain parses multisensory input despite the variable and often large differences in the onset of sensory signals across different modalities remains unclear. We can maintain a coherent multisensory percept across a considerable range of spatial and temporal discrepancies (1-4): For example, auditory and visual speech signals can be perceived as belonging to the same multisensory "object" over temporal windows of hundreds of milliseconds (5-7). However, such misalignment can drastically affect neuronal responses in ways that may also differ between brain regions (8-10). We asked how natural asynchronies in the onset of face/voice content in communication signals would affect voice-sensitive cortex, a region in the ventral "object" pathway (11) where neurons (i) are selective for auditory features in communication sounds (12)(13)(14), (ii) are influenced by visual "face" content (12), and (iii) display relatively slow and temporally variable responses in comparison with neurons in primary auditory cortical or subcortical structures (14-16).Neurophysiological studies in human and nonhuman animals have provided considerable insights into the role of cortical oscillations during multisensory conditions and for parsing speech. Cortical oscillations entrain to the slow temporal dynamics of natural sounds (17)(18)(19)(20) and are thought to reflect the excitability of local networks to sensory inputs...

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Section: Discussioncontrasting

confidence: 77%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Natural asynchronies in audiovisual communication signals regulate neuronal multisensory interactions in voice-sensitive cortex

Perrodin

Kayser

Logothetis

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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“…These units should also be selective for specific phoneme orderings. Nonhuman primate data for regions rostral to A1 confirm that latencies increase rostrally along the ventral stream (34,55,68,69), with the median latency to peak response approaching 100 ms in area RT (34), consistent with the latencies required for phonetic concatenation. In a rare human electrophysiology study, Creutzfeldt and colleagues (1989) report vigorous single-unit responses to words and sentences in mid-to anterior STG (70).…”

mentioning

confidence: 55%

“…As human core auditory fields lie along or about Heschl's gyrus (13,(57)(58)(59)100), the ventral streams' course can be inferred to traverse portions of planum temporale. Specifically, the ventral stream is associated with macaque areas RTp and AL (54)(55)(56), which lie anterior to and lateral of A1 (13). As human A1 lies on or about the medial aspect of Heschl's gyrus, with core running along its extent (57, 100), a processing cascade emanating from core areas, progressing both laterally, away from core itself, and anteriorly, away from A1, will necessarily traverse the anterior-lateral portion of planum temporale.…”

Section: And Cohen and Colleagues' (2004) Hypothesis Of An Auditory Wmentioning

confidence: 99%

Phoneme and word recognition in the auditory ventral stream

DeWitt

Rauschecker²

2012

Proc. Natl. Acad. Sci. U.S.A.

419

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Spoken word recognition requires complex, invariant representations. Using a meta-analytic approach incorporating more than 100 functional imaging experiments, we show that preference for complex sounds emerges in the human auditory ventral stream in a hierarchical fashion, consistent with nonhuman primate electrophysiology. Examining speech sounds, we show that activation associated with the processing of short-timescale patterns (i.e., phonemes) is consistently localized to left mid-superior temporal gyrus (STG), whereas activation associated with the integration of phonemes into temporally complex patterns (i.e., words) is consistently localized to left anterior STG. Further, we show left midto anterior STG is reliably implicated in the invariant representation of phonetic forms and that this area also responds preferentially to phonetic sounds, above artificial control sounds or environmental sounds. Together, this shows increasing encoding specificity and invariance along the auditory ventral stream for temporally complex speech sounds.functional MRI | meta-analysis | auditory cortex | object recognition | language S poken word recognition presents several challenges to the brain. Two key challenges are the assembly of complex auditory representations and the variability of natural speech (SI Appendix, Fig. S1) (1). Representation at the level of primary auditory cortex is precise: fine-grained in scale and local in spectrotemporal space (2, 3). The recognition of complex spectrotemporal forms, like words, in higher areas of auditory cortex requires the transformation of this granular representation into Gestalt-like, object-centered representations. In brief, local features must be bound together to form representations of complex spectrotemporal contours, which are themselves the constituents of auditory "objects" or complex sound patterns (4, 5). Next, representations must be generalized and abstracted. Coding in primary auditory cortex is sensitive even to minor physical transformations. Object-centered coding in higher areas, however, must be invariant (i.e., tolerant of natural stimulus variation) (6). For example, whereas the phonemic structure of a word is fixed, there is considerable variation in physical, spectrotemporal form-attributable to accent, pronunciation, body size, and the like-among utterances of a given word. It has been proposed for visual cortical processing that a feed-forward, hierarchical architecture (7) may be capable of simultaneously solving the problems of complexity and variability (8-12). Here, we examine these ideas in the context of auditory cortex.In a hierarchical pattern-recognition scheme (8), coding in the earliest cortical field would reflect the tuning and organization of primary auditory cortex (or core) (2, 3, 13). That is, single-neuron receptive fields (more precisely, frequency-response areas) would be tuned to particular center frequencies and would have minimal spectrotemporal complexity (i.e., a single excitatory zone and one-to-two inhibitory side bands). ...

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