The primate cortical auditory system and neural representation of conspecific vocalizations

Abstract

Over the past decade, renewed interest in the auditory system has resulted in a surge of anatomical and physiological research in the primate auditory cortex and its targets. Anatomical studies have delineated multiple areas in and around primary auditory cortex and demonstrated connectivity among these areas, as well as between these areas and the rest of the cortex, including prefrontal cortex. Physiological recordings of auditory neurons have found that species-specific vocalizations are useful in probing the selectivity and potential functions of acoustic neurons. A number of cortical regions contain neurons that are robustly responsive to vocalizations, and some auditory responsive neurons show more selectivity for vocalizations than for other complex sounds. Demonstration of selectivity for vocalizations has prompted the question of which features are encoded by higher-order auditory neurons. Results based on detailed studies of the structure of these vocalizations, as well as the tuning and information-coding properties of neurons sensitive to these vocalizations, have begun to provide answers to this question. In future studies, these and other methods may help to define the way in which cells, ensembles, and brain regions process communication sounds. Moreover, the discovery that several nonprimary auditory cortical regions may be multisensory and responsive to vocalizations with corresponding facial gestures may change the way in which we view the processing of communication information by the auditory system.

Figure 1

Organization of auditory cortex and connections. (a) Lateral brain schemata showing the location of auditory cortical areas in the macaque brain. (b) Magnified view of the primary auditory cortex, or core (purple) surrounded by the lateral and medial belt areas (yellow), which are bounded laterally by the parabelt cortex (orange). Connections between the core and belt areas are indicated. The progression of frequency tuning across an area is indicated by an “H” (high) or “L” (low). Projections to the cortex of the superior temporal sulcus (STS) and the prefrontal cortex are indicated with arrows. Diagram in (b) adapted from Hackett et al. 1998a.

Figure 2

Connections of the prefrontal cortex with physiologically characterized regions of the belt and parabelt auditory cortex. (a) Color-coded schematic of the core (purple) and belt (yellow) region of the auditory cortex. (b) Physiological map of recordings from the lateral belt region. Numbers indicate the best center frequency for each electrode penetration (black or white dots) in kHz. Injections of different anterograde and retrograde tracers (colored regions) are shown with respect to these recordings. The boundaries of antero-lateral belt auditory cortex (AL), middle-lateral belt auditory cortex (ML), and caudal-lateral belt auditory cortex (CL) are delineated by a bounded line and are derived from the frequency reversal points. (c) Three coronal sections through the prefrontal cortex indicating anterograde and retrograde labeling, which resulted from the color-coded injections placed in the lateral belt/parabelt auditory cortex in (b). (d) A summary of the projections from rostral and caudal auditory cortex showing that dual streams emanating from the caudal and rostral auditory cortex innervate dorsal and ventral prefrontal cortex, respectfully. Adapted from Romanski et al. (1999b). NCR, no clear response.

Figure 3

Averaged responses to a natural twitter call and its time-reversed version recorded from the marmoset primary auditory cortex. Sampled cells in the auditory cortex were categorized as selective if they responded more to the natural twitter call than to the reversed twitter call, whereas nonselective units responded more to the reversal. The waveform of an example twitter call is shown in (a). In (b), the averaged PSTH (bin width = 2.0 ms) for the selective population of neurons (n = 93) to the forward twitter call is shown. In (c), the selective population response to the time-reversed version of the call is shown. Adapted from Wang (2000).

Figure 4

Bubble graph of co-occurrence of responses to macaque vocalizations in neurons that selectively responded to two out of seven macaque calls. The diameter of the circles is proportional to the number of co-occurrences (the scale bubble = 5 co-occurrences). Neurons responded with a higher probability to calls from the same phonetic category, as in the cases of “coo” and “harmonic arch,” which are both harmonic calls, or “bark” and “growl,” which are both noisy calls. Semantic category, does not seem to play a major role at this processing level. Adapted from Tian et al. (2001).

Figure 5

Multisensory responsive neuron in the lateral belt auditory cortex of the macaque monkey. A single-unit response to a species-specific vocalization, a coo (voice, green), the corresponding dynamic movie of that vocalization (face, blue), and their simultaneous presentation (face + voice, red) are shown in the top panel as smoothed histograms and in the bottom raster panels. The response to the face voice condition elicited an increase in response compared with the voiceor face-alone conditions. Adapted+from Ghazanfar et al. (2008).

Figure 6

Location of auditory responsive cells in the ventrolateral prefrontal cortex (PFC). On the left, three coronal sections with electrode tracks (red lines) are shown with locations of auditory responsive cells indicated (black tics). On the right, the lateral surface of the macaque brain is shown, indicating the location from which the coronal sections were taken (black lines, A, B, C) and from where auditory responsive cells were recorded as red dots in areas 12 and 45, which make up the VLPFC. The blue shaded area within area 45 delimits the region in which visually responsive neurons, including face cells, were found. Abbreviations: ps, principal sulcus; as, arcuate sulcus; los, lateral orbital sulcus.

Figure 7

Types of responses to auditory stimuli by prefrontal neurons. The responses of two single units to three different exemplars of auditory stimuli are shown in raster and histogram plots. The onset of the auditory stimulus (vocalizations in the first two rows; noise and other stimuli in the last row) is at time “0” and the duration of the stimulus is depicted by the length of the gray bar. A neuron with a phasic response to the onset of auditory stimuli is shown in (a) and a neurons that produced a sustained response to auditory stimuli is depicted in (b).

Figure 8

A comparison of selectivity to vocalizations (a) ventral prefrontal cortex (VLPFC) and (b) the anterior lateral belt (AL). The number of cells responding to one or more vocalizations on the basis of the neuron’s half-peak response to all stimuli (Romanski et al. 2005, Rauschecker et al. 1995) is shown in the bar graph.

Figure 9

Total information (in bits) and average percent correct (as percent × 0.01). The graph shows a tuning curve for the population average of VLPFC cells rank-ordered according to optimum vocalization. On average, VLPFC cells contain 1.3 bits of information (red line) about the optimum vocalization and 1 bit about the second-best vocalization. This result drops abruptly for additional vocalizations. In terms of the decoding analysis (percent correct, blue line), VLPFC cells are above chance (which would be 10%) at discriminating the best vocalization for any given neuron, and this number drops abruptly beyond two vocalizations. Adapted from Romanski et al. (2005).

Figure 10

Typical prefrontal responses to macaque vocalizations and cluster analysis of mean response. (a, d) The neuronal response to 5 of 10 vocalization stimuli that were presented during passive fixation is shown (spike density function). (b, e) The mean response to all 10 vocalizations is depicted in a bar graph. (c, f) The dendrogram created from a hierarchical cluster analysis of the mean response is shown. Auditory responses that are similar cluster together, and these clusters are color-coded to match the bar graph and spike density function graphs. The cell in (a) responded best to the warble and coo stimuli, which are acoustically similar and to which the neuron responded in a similar manner. The cell in (b) responded best to 2 types of screams. The responses to these stimuli were similar and clustered together as shown in the dendrogram (f).

Figure 11

Vocalization and neuronal consensus trees. Consensus trees, based on the dendrograms for the vocalizations analyzed in Averbeck & Romanski (2006) (a) and for the neuronal response to the vocalizations reported in Romanski et al. 2005 (b) are shown. (a) Dendrograms were derived for each of the 12 vocalization lists, and a consensus tree of these was generated to indicate the common groupings according to acoustic features of the vocalizations. Warbles and coos and aggressive calls and grunts are two groups that occurred in the analysis of the vocalizations and in the analysis of the neural response to those vocalizations. Adapted from Romanski et al. (2005).

Figure 12

Principal and independent component filtering of a coo. (a) Spectrogram of an unfiltered coo. (b) Spectrogram of a coo after projecting into the first 10 principal components. (c) Spectrogram of a coo after projecting into the first 10 independent components. (d) Fourier representation of the first principal component. (e) Fourier representation of the first independent component. (f) Time representation of the first principal component. (g) Time representation of the first independent component.

Figure 13

Single neuron responses to original and filtered calls. (a) Response of a single VLPFC neuron to a shrill bark and to the same shrill bark filtered with either the first 10 principal or independent components. (b,d) Mean firing rate to original and filtered calls. (c) Response of a single VLPFC neuron to a grunt and the same grunt filtered with either the first 10 principal or independent components.

Figure 14

Temporal and spectral modulation of coo and gekker. (a, top) Spectrogram of coo. (Second from top) Modulation spectra of coo (i.e., Fourier transform of spectrogram). (Third from top) Average frequency modulation, computed by averaging across the time dimension in modulation spectra. (Bottom) Average temporal modulation, computed by averaging across the frequency dimension in modulation spectra.

Figure 15

Spectrogram (a) and time-probability plot (b) of a copulation scream. Time probability values were generated by the Hidden Markov Model.

Figure 16

Multisensory neuronal responses in prefrontal cortex (PFC). The responses of two single units are shown in (a) and (b) as raster/spike density plots to a vocalization alone (Audio, A) and a face (Visual, V) and both presented simultaneously (Audio-Visual, AV). A bar graph of the mean response to these stimuli is shown at the right. The cell in (a) exhibited multisensory enhancement, and the cell in (b) demonstrated multisensory suppression. The location where multisensory neurons (red circles) were found in the VLPFC is depicted on a lateral view of the frontal lobe in (c).