Neural Representation of Harmonic Complex Tones in Primary Auditory Cortex of the Awake Monkey

Some musical chords sound pleasant, or consonant, while others sound unpleasant, or dissonant. Helmholtz's psychoacoustic theory of consonance and dissonance attributes the perception of dissonance to the sensation of “beats” and “roughness” caused by interactions in the auditory periphery between adjacent partials of complex tones comprising a musical chord. Conversely, consonance is characterized by the relative absence of beats and roughness. Physiological studies in monkeys suggest that roughness may be represented in primary auditory cortex (A1) by oscillatory neuronal ensemble responses phase-locked to the amplitude-modulated temporal envelope of complex sounds. However, it remains unknown whether phase-locked responses also underlie the representation of dissonance in auditory cortex. In the present study, responses evoked by musical chords with varying degrees of consonance and dissonance were recorded in A1 of awake macaques and evaluated using auditory-evoked potential (AEP), multiunit activity (MUA), and current-source density (CSD) techniques. In parallel studies, intracranial AEPs evoked by the same musical chords were recorded directly from the auditory cortex of two human subjects undergoing surgical evaluation for medically intractable epilepsy. Chords were composed of two simultaneous harmonic complex tones. The magnitude of oscillatory phase-locked activity in A1 of the monkey correlates with the perceived dissonance of the musical chords. Responses evoked by dissonant chords, such as minor and major seconds, display oscillations phase-locked to the predicted difference frequencies, whereas responses evoked by consonant chords, such as octaves and perfect fifths, display little or no phase-locked activity. AEPs recorded in Heschl's gyrus display strikingly similar oscillatory patterns to those observed in monkey A1, with dissonant chords eliciting greater phase-locked activity than consonant chords. In contrast to recordings in Heschl's gyrus, AEPs recorded in the planum temporale do not display significant phase-locked activity, suggesting functional differentiation of auditory cortical regions in humans. These findings support the relevance of synchronous phase-locked neural ensemble activity in A1 for the physiological representation of sensory dissonance in humans and highlight the merits of complementary monkey/human studies in the investigation of neural substrates underlying auditory perception.

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A complex feature-based representation of vocalizations emerges in the superficial layers of primary auditory cortex

10.1101/2021.04.11.439331 ◽

2021 ◽

Author(s):

Pilar Montes-Lourido ◽

Manaswini Kar ◽

Stephen V David ◽

Srivatsun Sadagopan

Keyword(s):

Auditory Cortex ◽

Auditory Processing ◽

Receptive Fields ◽

Primary Auditory Cortex ◽

Neural Representation ◽

Specific Information ◽

Intermediate Step ◽

Stimulus Information ◽

Feature Selectivity ◽

Feature Based

Early in auditory processing, neural responses faithfully reflect acoustic input. At higher stages of auditory processing, however, neurons become selective for particular call types, eventually leading to specialized regions of cortex that preferentially process calls at the highest auditory processing stages. We previously proposed that an intermediate step in how non-selective responses are transformed into call-selective responses is the detection of informative call features. But how neural selectivity for informative call features emerges from non-selective inputs, whether feature selectivity gradually emerges over the processing hierarchy, and how stimulus information is represented in non-selective and feature-selective populations remain open questions. In this study, using unanesthetized guinea pigs, a highly vocal and social rodent, as an animal model, we characterized the neural representation of calls in three auditory processing stages: the thalamus (vMGB), and thalamorecipient (L4) and superficial layers (L2/3) of primary auditory cortex (A1). We found that neurons in vMGB and A1 L4 did not exhibit call-selective responses and responded throughout the call durations. However, A1 L2/3 neurons showed high call-selectivity with about a third of neurons responding to only one or two call types. These A1 L2/3 neurons only responded to restricted portions of calls suggesting that they were highly selective for call features. Receptive fields of these A1 L2/3 neurons showed complex spectrotemporal structures that could underlie their high call feature selectivity. Information theoretic analysis revealed that in A1 L4 stimulus information was distributed over the population and was spread out over the call durations. In contrast, in A1 L2/3, individual neurons showed brief bursts of high stimulus-specific information, and conveyed high levels of information per spike. These data demonstrate that a transformation in the neural representation of calls occurs between A1 L4 and A1 L2/3, leading to the emergence of a feature-based representation of calls in A1 L2/3. Our data thus suggest that observed cortical specializations for call processing emerge in A1, and set the stage for further mechanistic studies.

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Erratum to “Neural correlates of auditory stream segregation in primary auditory cortex of the awake monkey”

Hearing Research ◽

10.1016/s0378-5955(01)00260-x ◽

2001 ◽

Vol 155 (1-2) ◽

pp. 183

Author(s):

Yonatan I. Fishman ◽

David H. Reser ◽

Joseph C. Arezzo ◽

Mitchell Steinschneider

Keyword(s):

Auditory Cortex ◽

Primary Auditory Cortex ◽

Neural Correlates ◽

Stream Segregation ◽

Auditory Stream ◽

Auditory Stream Segregation ◽

Awake Monkey

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Spectral response patterns of auditory cortex neurons to harmonic complex tones in alert monkey (Macaca mulatta)

Journal of Neurophysiology ◽

10.1152/jn.1990.64.1.282 ◽

1990 ◽

Vol 64 (1) ◽

pp. 282-298 ◽

Cited By ~ 51

Author(s):

D. W. Schwarz ◽

R. W. Tomlinson

Keyword(s):

Receptive Field ◽

Auditory Cortex ◽

Pure Tone ◽

Response Pattern ◽

Response Patterns ◽

Neuronal Responses ◽

Harmonic Complex ◽

Tuning Curves ◽

Complex Tones ◽

Pure Tones

1. The auditory cortex in the superior temporal region of the alert rhesus monkey was explored for neuronal responses to pure and harmonic complex tones and noise. The monkeys had been previously trained to recognize the similarity between harmonic complex tones with and without fundamentals. Because this suggested that they could preceive the pitch of the lacking fundamental similarly to humans, we searched for neuronal responses relevant to this perception. 2. Combination-sensitive neurons that might explain pitch perception were not found in the surveyed cortical regions. Such neurons would exhibit similar responses to stimuli with similar periodicities but differing spectral compositions. The fact that no neuron with responses to a fundamental frequency responded also to a corresponding harmonic complex missing the fundamental indicates that cochlear distortion products at the fundamental may not have been responsible for missing fundamental-pitch perception in these monkeys. 3. Neuronal responses can be expressed as relatively simple filter functions. Neurons with excitatory response areas (tuning curves) displayed various inhibitory sidebands at lower and/or higher frequencies. Thus responses varied along a continuum of combined excitatory and inhibitory filter functions. 4. Five elementary response classes along this continuum are presented to illustrate the range of response patterns. 5. “Filter (F) neurons” had little or no inhibitory sidebands and responded well when any component of a complex tone entered its pure-tone receptive field. Bandwidths increased with intensity. Filter functions of these neurons were thus similar to cochlear nerve-fiber tuning curves. 6. ”High-resolution filter (HRF) neurons” displayed narrow tuning curves with narrowband widths that displayed little growth with intensity. Such cells were able to resolve up to the lowest seven components of harmonic complex tones as distinct responses. They also responded well to wideband stimuli. 7. “Fundamental (F0) neurons” displayed similar tuning bandwidths for pure tones and corresponding fundamentals of harmonic complexes. This response pattern was due to lower harmonic complexes. This response pattern was due to lower inhibitory sidebands. Thus these cells cannot respond to missing fundamentals of harmonic complexes. Only physically present components in the pure-tone receptive field would excite such neurons. 8. Cells with no or very weak responses to pure tones or other narrowband stimuli responded well to harmonic complexes or wideband noise.(ABSTRACT TRUNCATED AT 400 WORDS)

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Spectro-Temporal Response Field Characterization With Dynamic Ripples in Ferret Primary Auditory Cortex

Journal of Neurophysiology ◽

10.1152/jn.2001.85.3.1220 ◽

2001 ◽

Vol 85 (3) ◽

pp. 1220-1234 ◽

Cited By ~ 238

Author(s):

Didier A. Depireux ◽

Jonathan Z. Simon ◽

David J. Klein ◽

Shihab A. Shamma

Keyword(s):

Transfer Function ◽

Auditory Cortex ◽

Cross Sections ◽

Primary Auditory Cortex ◽

Neural Representation ◽

Temporal Response ◽

Spectral Functions ◽

Linear Dynamics ◽

Complex Response ◽

Response Field

To understand the neural representation of broadband, dynamic sounds in primary auditory cortex (AI), we characterize responses using the spectro-temporal response field (STRF). The STRF describes, predicts, and fully characterizes the linear dynamics of neurons in response to sounds with rich spectro-temporal envelopes. It is computed from the responses to elementary “ripples,” a family of sounds with drifting sinusoidal spectral envelopes. The collection of responses to all elementary ripples is the spectro-temporal transfer function. The complex spectro-temporal envelope of any broadband, dynamic sound can expressed as the linear sum of individual ripples. Previous experiments using ripples with downward drifting spectra suggested that the transfer function is separable, i.e., it is reducible into a product of purely temporal and purely spectral functions. Here we measure the responses to upward and downward drifting ripples, assuming reparability within each direction, to determine if the total bidirectional transfer function is fully separable. In general, the combined transfer function for two directions is not symmetric, and hence units in AI are not, in general, fully separable. Consequently, many AI units have complex response properties such as sensitivity to direction of motion, though most inseparable units are not strongly directionally selective. We show that for most neurons, the lack of full separability stems from differences between the upward and downward spectral cross-sections but not from the temporal cross-sections; this places strong constraints on the neural inputs of these AI units.

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