Morphology and Computation

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F Comparison of population mean of fractional contact call duration normalized to the air condition. Bar height represents population mean. Error bars are SEM. Each line is one subject. G, I Proportions of trills and twitters over postnatal days for air and heliox conditions. H, J Comparison of heliox effect on trill and twitter proportion. K Heliox effect on sound spectrum. Left panels: mean power spectral densities PSDs of different call types in air condition.

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Right panels: mean PSDs in heliox condition. As predicted by the model, placing infant marmosets in heliox increased the proportion of trills and twitters and decreased the duration of contact calls, a reversal of the vocal development trend Fig 4D. Fig 4E, 4G and 4I show the developmental trajectories of these three call types produced in and out of the heliox environment for all three infants.

For all of them, more trills and twitters are produced, and the duration of contact calls shortened, in heliox. Fig 4F, 4H and 4J show the mean change between heliox and air over the first two months. The proportion of trills increased significantly in heliox Contact call duration under heliox condition dropped by Increased call rates are typically associated with increased arousal levels [ 46 ]. These data demonstrate that developmental changes in lung capacity can account for the changing call types produced as the infant marmoset grows.

An additional possibility is that the heliox makes it easier to produce trills and twitters via a laryngeal influence, as it is well known that heliox can affect the spectral properties of vocalizations [ 47 — 49 ]. However, heliox does not have a large effect on the fundamental frequency F0 [ 47 — 49 ], and the F0 represents the source sound coming directly from the larynx [ 50 ].

We calculated the mean power spectral density PSD of contact calls, trills, and twitters across all postnatal days in both heliox and air. All three call types had nearly identical F0s Fig 4K , left panels. In contrast, the F0s of the tonal calls were increased by only approximately 1. Thus, the heliox effect on the spectral properties of vocalizations was mostly passive and only minimally due to changes in the effort for laryngeal control.

That the heliox environment had largely the same effect on all three call types suggests that air density does not differentially benefit the production of trills and twitters. Vocal development is a consequence of many interacting factors, including the growth of the vocal apparatus, the muscles that innervate it, the nervous system that controls those muscles, and social interactions that adjust nervous system function via experience [ 34 , 51 ].

Previous efforts isolated the role of social interactions on marmoset monkey vocal development [ 31 , 52 , 53 ]. Those studies took advantage of individual differences in the amount of social feedback provided by parents while controlling for the contributions of body growth. They found that the rate of developmental changes in some acoustic parameters, such as the noisiness and amplitude modulation, could be attributed to the amount of social feedback provided by parents, while other parameters, such as duration, dominant frequency, and the disappearance of calls produced in the incorrect context, could not be explained with such experience-dependent mechanisms [ 31 , 52 ].

Conversely, in the current work, we found that increases in call duration and changes in call usage i. Our model of interconnected laryngeal and respiratory CPGs predicted that if the respiratory CPG received sensory feedback from growing lungs, the production of trills and twitters would decrease and the production and duration of contact calls would increase. No changes in the neural properties of the CPGs were required.

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These predictions were empirically tested by recording the vocalizations of infant marmosets in a heliox environment. Akin to placing infants in water to reduce the load on stepping behavior [ 27 ], placing vocalizing infant marmoset monkeys in heliox reduces the respiratory load, thereby increasing the number of trills and twitter calls and shortening the contact calls.

Thus, in contrast to the strong emphasis on neural changes typically used to explain vocal development [ 22 ], these data support the idea that some aspects of vocal development can occur through morphological computation: The body in this case, the growing lungs can be exploited as a computational resource by reducing the number of control parameters that need to be tracked and adjusted by the nervous system [ 3 — 5 ].

Respiration plays a key role in both vocal production [ 8 , 54 ] and behavior in general [ 55 ]. Respiration and locomotion, for instance, are synchronized to different extents depending on the mechanical constraints imposed by posture and body size on respiration [ 56 ]. The upright posture of humans reduces the influence of gait on respiration, allowing more flexibility in respiratory patterning [ 56 ]. Our model proposed that morphological computation through lung growth benefited the neural control of vocalization by weakening the coupling of respiration from laryngeal movements.

The apparent decoupling of respiration from laryngeal influence by lung growth in marmosets may in the same way allow more independent control of respiration, thereby improving the accuracy of vocal communication.

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Naturally, our model is a simplification of what is known about the dense, multinode network of vocalization-related CPGs [ 10 ], which includes within it a complicated network of respiratory CPGs [ 57 ]. Nevertheless, our model and behavioral data provide supportive evidence for the hypothesis that the intrinsic properties and connectivity of these networks need not change over the course of vocal development to account for some dramatic shifts in vocal output. It is important to note that the model presented in this work provides only one possible solution to the structure of the neural activity that can generate sequences of marmoset infant vocalizations.

We did not design the model to simulate any specific neuroanatomical or neurophysiological details, as these are not yet well understood. Rather, we use the model as a way to extract a low-dimensional representation of this complex vocal behavior. There are other dynamical models with similar structures that can also lead to the same results. For example, an alternative setup of the CPG model would be one with articulate CPGs driving different neural populations that, respectively, drive the laryngeal and respiratory muscles.

Although our study suggested that the bifurcations that create different vocal patterns occur at the level of subcortical CPGs, it is not sufficient to refute the alternative possibility that forebrain structures might also play an important role [ 14 , 58 , 59 ]. This integrative understanding is important from a clinical perspective as well. Human infants who do not vocalize a lot tend to be fed and held less by mothers, and are slowed in their speech development [ 60 ].

The subjects are infant common marmosets Callithrix jacchus housed at Princeton University. The subjects were all born in captivity and raised by family. All subjects, including all other members in the family, received water ad libitum and were fed with standard commercial chow supplemented with fruits and vegetables. In the search for an appropriate model, we considered a two-dimensional system for each CPG oscillator to allow Hopf bifurcations.

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We also looked for a model that contains two regions of stable fixed points separated by a limit cycle region via Hopf bifurcations. For simplicity, we start building the dynamical model for each oscillator from a simple 2D system in which f x , y , a is a polynomial up to the third order and a is a parameter. With these simplifications, the Jacobian matrix has the form. In addition, to allow oscillations in the middle range of a , the parabola is inverted. Again, we drop h y for simplicity.

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We also introduce a coupling term from the other oscillator in the equation and a time constant to change the oscillating frequency. We define for the relative drive strength. The parameters of the model are listed in Table 1.

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  • We saturated x 1 and x 2 with sigmoid functions to get biologically reasonable p air pressure, varying between— p 0 and p 0 and k laryngeal tension, varying between 0 and k 0 :. With different values of the input, different dynamics were produced. As different call types are characterized by different duration Fig 1B , we used the spectrum of x 1 to find the regimes for different calls.

    The regions for different call types were identified based on the oscillatory frequencies. Call proportions were estimated as the range for call type i in the [0, 1] range. To estimate the goodness of fit, we calculated the R 2 between data and simulated proportions. Starting from P1, marmoset infants were placed in an induction chamber that holds approximately 45 L of air.

    The subjects were introduced into the chamber through the lid on top of the chamber. An air flow meter was attached to the inlet. To reduce echoes, acoustic foam was attached to the walls of the chamber.

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    In the control condition, we replaced the solid lid with a perforated lid. In each session, we carried out recordings of 10 min in heliox and 10 min in air. The order of these two conditions alternated every session. Since it requires 5 min for the gas to fill up the chamber, we discarded the first 5-min recording in both heliox and air conditions in the analysis. Heliox was provided constantly through the heliox session. To control the auditory effect from the heliox injection, we recorded the sound of airflow in the chamber and played it through a Bluetooth speaker Lyrix Jive Jumbo placed in the chamber near the inlet during the control condition.

    The sound pressure level of the playback was calibrated the same as the actual airflow sound using a sound level meter Extech An HD webcam Logitech Ce was placed in front of the chamber facing the side where there was no foam attached to record the abdominal movement during vocalization at 30 fps.

    To test if the heliox approach was effective, we extracted respiratory pattern of the abdominal movement duration vocalization from video recordings Logitech Ce. Phonation requires about 5- to fold of pressure more than the baseline breathing, and therefore, it depends upon the abdominal sheet to drive active expiration during vocalization [ 61 , 62 ]. We extracted abdominal movements from video recordings in two marmosets who were approximately 2 months old during the production of phee calls in air and heliox environments.

    Infants at this age essentially only produce phee calls in isolation. Movie clips during phee call production were segmented using Windows Movie Maker. The marmosets usually stay still during vocalization, and so we could select a rectangular area around the abdomen through the frame stack and track its movements during the time window of vocalization. The RGB images were converted to grayscale by taking the mean across the color dimensions.

    The areas were first vectorized and converted into a matrix with rows representing frames and columns representing pixels. Principle component analysis was carried out to capture frame-to-frame variations related to respiration. The principle components were then aligned with the sound signal, and the PCs that were correlated with vocal production were selected to represent the abdominal movements during vocalization S2A Fig.

    The average traces of the video extractions were calculated from the resampled data at Hz and were aligned to the call onsets.

    We averaged the traces of the same condition. To compare the respiratory rates in different conditions, we calculated number of cycles per s using number of cycles divided by total call duration. Call types were first categorized automatically based on duration and Wiener entropy and then manually inspected. Duration was calculated as the duration of individual utterances within a call.

    Consecutive utterances in the same category with no more than 0. Each point of the call type proportions was calculated by grouping two consecutive, counterbalanced sessions. Call proportions were calculated as the number of calls of a specific type divided by the total number of calls in this condition. The corresponding postnatal days were calculated as the mean of the two consecutive days.

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