Response of wild songbirds to songs synthesized with a low-dimensional model.

In this work, we used a dynamical system derived from an avian vocal production model to generate synthetic songs that mimic the Zonotrichia capensis songs. We confirmed that these synthetic renditions elicited behavioral responses similar to those evoked by real songs in wild songbirds of the same species. Specifically, we observed an increase in the singing rate of individual birds when a playback device was introduced into their territories. The success of our approach instills confidence in the hypotheses underpinning the model and provides a valuable tool for investigating a wide range of biological questions.

Synthesizing avian dreams.

During sleep, sporadically, it is possible to find neural patterns of activity in areas of the avian brain that are activated during the generation of the song. It has recently been found that in the vocal muscles of a sleeping bird, it is possible to detect activity patterns during these silent replays. In this work, we employ a dynamical systems model for song production in suboscine birds in order to translate the vocal muscles activity during sleep into synthetic songs. Besides allowing us to translate muscle activity into behavior, we argue that this approach poses the biomechanics as a unique window into the avian brain, with biophysical models as its probe.

The dynamics behind diversity in suboscine songs.

Vocal behavior plays a crucial evolutionary role. In the case of birds, song is critically important in courtship, male-male competition and other key behaviors linked to reproduction. However, under natural conditions, a variety of avian species live in close proximity and share an 'acoustic landscape'. Therefore, they need to be able to differentiate their calls or songs from those of other species and also from those of other individuals of the same species. To do this efficiently, birds display a remarkable diversity of sounds. For example, in the case of vocal learners, such as oscine passerines (i.e. songbirds), complex sequences and subtle acoustic effects are produced through the generation of complex neuromuscular instructions driving the vocal organ, which is remarkably conserved across approximately 4000 oscine species. By contrast, the majority of the sister clade of oscines, the suboscine passerines, are thought not to be vocal learners. Despite this, different suboscine species can generate a rich variety of songs and quite subtle acoustic effects. In the last few years, different suboscine species have been shown to possess morphological adaptations that allow them to produce a diversity of acoustic characteristics. Here, we briefly review the mechanisms of sound production in birds, before considering three suboscine species in more detail. The examples discussed in this Review, integrating biological experiments and biomechanical modeling using non-linear dynamical systems, illustrate how a morphological adaptation can produce complex acoustic properties without the need for complex neuromuscular control.

Multifractal analysis of birdsong and its correlation structure.

The time series recordings of typical songs of songbirds exhibit highly complex and structured behavior, which is characteristic of their species and stage of development, and need to be analyzed by methods that can uncover their correlation structure. Here we analyze a typical song of a canary using Hurst exponents and multifractal analysis, which uncovers the correlation structure of typical song segments. These are then compared with the corresponding quantities from shuffled data, which destroys the temporal correlations and iterative amplitude-adjusted Fourier transform (IAAFT) data. It is seen that temporal correlations are responsible for the multifractal behavior seen in the data and that two-point correlations, which are preserved by the transform, are important in the high-fluctuation regime. Higher-order correlations and intersyllabic gaps dominate the behavior of the low-fluctuation regime. These observations are supported by the simplicial characterization of the corresponding time series networks. Complexity measures are also used to analyze the amplitude envelope time series. These indicate that intersyllabic gaps contribute a significant fraction to the complexity of the birdsong. Our method provides a detailed characterization of the data, which can enable the comparison of real and synthetic birdsong and comparisons across stages of development and species. A brief comparison with the song of the zebra finch supports this.

Neural oscillations are locked to birdsong rhythms in canaries.

How vocal communication signals are represented in the cortex is a major challenge for behavioural neuroscience. Beyond a descriptive code, it is relevant to unveil the dynamical mechanism responsible for the neural representation of auditory stimuli. In this work, we report evidence of synchronous neural activity in nucleus HVC, a telencephalic area of canaries (Serinus canaria), in response to auditory playback of the bird's own song. The rhythmic features of canary song allowed us to show that this large-scale synchronization was locked to defined features of the behaviour. We recorded neural activity in a brain region where sensorimotor integration occurs, showing the presence of well-defined oscillations in the local field potentials, which are locked to song rhythm. We also show a correspondence between local field potentials, multiunit activity and single unit activity within the same brain region. Overall, our results show that the rhythmic features of the vocal behaviour are represented in a telencephalic region of canaries.

Synthetic Birdsongs as a Tool to Induce, and Iisten to, Replay Activity in Sleeping Birds.

Birdsong is a complex vocal behavior, which emerges out of the interaction between a nervous system and a highly nonlinear vocal device, the syrinx. In this work we discuss how low dimensional dynamical systems, interpretable in terms of the biomechanics involved, are capable of synthesizing realistic songs. We review the experimental and conceptual steps that lead to the formulation of low dimensional dynamical systems for the song system and describe the tests that quantify their success. In particular, we show how to evaluate computational models by comparing the responses of highly selective neurons to the bird's own song and to synthetic copies generated mathematically. Beyond testing the hypothesis behind the model's construction, these low dimensional models allow designing precise stimuli in order to explore the sensorimotor integration of acoustic signals.

Replay of innate vocal patterns during night sleep in suboscines.

Activation of forebrain circuitry during sleep has been variably characterized as 'pre- or replay' and has been linked to memory consolidation. The evolutionary origins of this mechanism, however, are unknown. Sleep activation of the sensorimotor pathways of learned birdsong is a particularly useful model system because the muscles controlling the vocal organ are activated, revealing syringeal activity patterns for direct comparison with those of daytime vocal activity. Here, we show that suboscine birds, which develop their species-typical songs innately without the elaborate forebrain-thalamic circuitry of the vocal learning taxa, also engage in replay during sleep. In two tyrannid species, the characteristic syringeal activation patterns of the song could also be identified during sleep. Similar to song-learning oscines, the burst structure was more variable during sleep than daytime song production. In kiskadees (Pitangus sulphuratus), a second vocalization, which is part of a multi-modal display, was also replayed during sleep along with one component of the visual display. These data show unambiguously that variable 'replay' of stereotyped vocal motor programmes is not restricted to programmes confined within forebrain circuitry. The proposed effects on vocal motor programme maintenance are, therefore, building on a pre-existing neural mechanism that predates the evolution of learned vocal motor behaviour.

Birds breathe at an aerodynamic resonance.

We present a dynamical model for the avian respiratory system and report the measurement of its variables in normal breathing canaries (Serinus canaria). Fitting the parameters of the model, we are able to show that the birds in our study breathe at an aerodynamic resonance of their respiratory system. For different respiratory regimes, such as singing, where rapid respiratory gestures are used, the nonlinearities of the model lead to a shift in its resonances toward higher frequency values.

The structure of reconstructed flows in latent spaces.

Reconstructing the flow of a dynamical system from experimental data has been a key tool in the study of nonlinear problems. It allows one to discover the equations ruling the dynamics of a system as well as to quantify its complexity. In this work, we study the topology of the flow reconstructed by autoencoders, a dimensionality reduction method based on deep neural networks that has recently proved to be a very powerful tool for this task. We show that, although in many cases proper embeddings can be obtained with this method, it is not always the case that the topological structure of the flow is preserved.

Dynamical model for the neural activity of singing Serinus canaria.

Vocal production in songbirds is a key topic regarding the motor control of a complex, learned behavior. Birdsong is the result of the interaction between the activity of an intricate set of neural nuclei specifically dedicated to song production and learning (known as the "song system"), the respiratory system and the vocal organ. These systems interact and give rise to precise biomechanical motor gestures which result in song production. Telencephalic neural nuclei play a key role in the production of motor commands that drive the periphery, and while several attempts have been made to understand their coding strategy, difficulties arise when trying to understand neural activity in the frame of the song system as a whole. In this work, we report neural additive models embedded in an architecture compatible with the song system to provide a tool to reduce the dimensionality of the problem by considering the global activity of the units in each neural nucleus. This model is capable of generating outputs compatible with measurements of air sac pressure during song production in canaries (Serinus canaria). In this work, we show that the activity in a telencephalic nucleus required by the model to reproduce the observed respiratory gestures is compatible with electrophysiological recordings of single neuron activity in freely behaving animals.

Unusual Avian Vocal Mechanism Facilitates Encoding of Body Size.

In this work we study the sound production mechanism of the raspy sounding song of the white-tipped plantcutter (Phytotoma rutila), a species with a most unusual vocalization. The biomechanics involved in the production of this song, and scaling arguments, allowed us to predict the precise way in which body size is encoded in its vocalizations. We tested this prediction through acoustic analysis of recorded songs, computational modeling of its unusual vocal strategy, and inspection of museum specimens captured across southeastern and south-central South America.

Dynamics behind rough sounds in the song of the Pitangus sulphuratus.

The complex vocalizations found in different bird species emerge from the interplay between morphological specializations and neuromuscular control mechanisms. In this work we study the dynamical mechanisms used by a nonlearner bird from the Americas, the suboscine Pitangus sulphuratus, in order to achieve a characteristic timbre of some of its vocalizations. By measuring syringeal muscle activity, air sac pressure, and sound as the bird sings, we are able to show that the birds of this species manage to lock the frequency difference between two sound sources. This provides a precise control of sound amplitude modulations, which gives rise to a distinct timbral property.

Gating related activity in a syringeal muscle allows the reconstruction of zebra finches songs.

Birdsong production involves the simultaneous and precise control of a set of muscles that change the configuration and dynamics of the vocal organ. Although it has been reported that each one of the different muscles is primarily involved in the control of one acoustic feature, recent advances have shown that they act synergistically to achieve the dynamical state necessary for phonation. In this work, we present a set of criteria that allow the extraction of gating-related information from the electromyographic activity of the syringealis ventralis muscle, a muscle that has been shown to be involved in frequency modulation. Using dynamical models of the muscle and syringeal dynamics, we obtain a full reconstruction of the zebra finch song using only the activity of this muscle.

Syringeal EMGs and synthetic stimuli reveal a switch-like activation of the songbird's vocal motor program.

The coordination of complex vocal behaviors like human speech and oscine birdsong requires fine interactions between sensory and motor programs, the details of which are not completely understood. Here, we show that in sleeping male zebra finches (Taeniopygia guttata), the activity of the song system selectively evoked by playbacks of their own song can be detected in the syrinx. Electromyograms (EMGs) of a syringeal muscle show playback-evoked patterns strikingly similar to those recorded during song execution, with preferred activation instants within the song. Using this global and continuous readout, we studied the activation dynamics of the song system elicited by different auditory stimuli. We found that synthetic versions of the bird's song, rendered by a physical model of the avian phonation apparatus, evoked very similar responses, albeit with lower efficiency. Modifications of autogenous or synthetic songs reduce the response probability, but when present, the elicited activity patterns match execution patterns in shape and timing, indicating an all-or-nothing activation of the vocal motor program.

Towards an integrated view of vocal development.

Vocal development is usually studied from the perspective of neuroscience. In this issue, Zhang and Ghazanfar propose a way in which body growth might condition the process. They study the vocalizations of marmoset infants with a wide range of techniques that include computational models and experiments that mimic growth reversal. Their results suggest that the qualitative changes that occur during development are rooted in the nonlinear interaction between the nervous system and the biomechanics involved in respiration. This work illustrates how an integrative approach enriches our understanding of behavior.

From electromyographic activity to frequency modulation in zebra finch song.

Behavior emerges from the interaction between the nervous system and peripheral devices. In the case of birdsong production, a delicate and fast control of several muscles is required to control the configuration of the syrinx (the avian vocal organ) and the respiratory system. In particular, the syringealis ventralis muscle is involved in the control of the tension of the vibrating labia and thus affects the frequency modulation of the sound. Nevertheless, the translation of the instructions (which are electrical in nature) into acoustical features is complex and involves nonlinear, dynamical processes. In this work, we present a model of the dynamics of the syringealis ventralis muscle and the labia, which allows calculating the frequency of the generated sound, using as input the electrical activity recorded in the muscle. In addition, the model provides a framework to interpret inter-syllabic activity and hints at the importance of the biomechanical dynamics in determining behavior.

Adult zebra finches rehearse highly variable song patterns during sleep.

Brain activity during sleep is fairly ubiquitous and the best studied possible function is a role in memory consolidation, including motor memory. One suggested mechanism of how neural activity effects these benefits is through reactivation of neurons in patterns resembling those of the preceding experience. The specific patterns of motor activation replayed during sleep are largely unknown for any system. Brain areas devoted to song production in the songbird brain exhibit spontaneous song-like activity during sleep, but single cell neural recordings did not permit detection of the specific song patterns. We have now discovered that this sleep activation can be detected in the muscles of the vocal organ, thus providing a unique window into song-related brain activity at night. We show that male zebra finches (Taeniopygia guttata) frequently exhibit spontaneous song-like activity during the night, but that the fictive song patterns are highly variable and uncoordinated compared to the highly stereotyped day-time song production. This substantial variability is not consistent with the idea that night-time activity replays day-time experiences for consolidation. Although the function of this frequent activation is unknown, it may represent a mechanism for exploring motor space or serve to generate internal error signals that help maintain the high stereotypy of day-time song. In any case, the described activity supports the emerging insight that brain activity during sleep may serve a variety of functions.

Nonlinear dynamics in the study of birdsong.

Birdsong, a rich and complex behavior, is a stellar model to understand a variety of biological problems, from motor control to learning. It also enables us to study how behavior emerges when a nervous system, a biomechanical device and the environment interact. In this review, I will show that many questions in the field can benefit from the approach of nonlinear dynamics, and how birdsong can inspire new directions for research in dynamics.

Evolution of Vocal Diversity through Morphological Adaptation without Vocal Learning or Complex Neural Control.

The evolution of complex behavior is driven by the interplay of morphological specializations and neuromuscular control mechanisms [1-3], and it is often difficult to tease apart their respective contributions. Avian vocal learning and associated neural adaptations are thought to have played a major role in bird diversification [4-8], whereas functional significance of substantial morphological diversity of the vocal organ remains largely unexplored. Within the most species-rich order, Passeriformes, "tracheophones" are a suboscine group that, unlike their oscine sister taxon, does not exhibit vocal learning [9] and is thought to phonate with tracheal membranes [10, 11] instead of the two independent sources found in other passerines [12-14]. Here we show tracheophones possess three sound sources, two oscine-like labial pairs and the unique tracheal membranes, which collectively represent the largest described number of sound sources for a vocal organ. Birds with experimentally disabled tracheal membranes were still able to phonate. Instead of the main sound source, the tracheal membranes constitute a morphological specialization, which, through interaction with bronchial labia, contributes to different acoustic features such as spectral complexity, amplitude modulation, and enhanced sound amplitude. In contrast, these same features arise in oscines from neuromuscular control of two labial sources [15-17]. These findings are supported by a modeling approach and provide a clear example for how a morphological adaptation of the tracheophone vocal organ can generate specific, complex sound features. Morphological specialization therefore constitutes an alternative path in the evolution of acoustic diversity to that of oscine vocal learning and complex neural control.

Temperature manipulation of neuronal dynamics in a forebrain motor control nucleus.

Different neuronal types within brain motor areas contribute to the generation of complex motor behaviors. A widely studied songbird forebrain nucleus (HVC) has been recognized as fundamental in shaping the precise timing characteristics of birdsong. This is based, among other evidence, on the stretching and the "breaking" of song structure when HVC is cooled. However, little is known about the temperature effects that take place in its neurons. To address this, we investigated the dynamics of HVC both experimentally and computationally. We developed a technique where simultaneous electrophysiological recordings were performed during temperature manipulation of HVC. We recorded spontaneous activity and found three effects: widening of the spike shape, decrease of the firing rate and change in the interspike interval distribution. All these effects could be explained with a detailed conductance based model of all the neurons present in HVC. Temperature dependence of the ionic channel time constants explained the first effect, while the second was based in the changes of the maximal conductance using single synaptic excitatory inputs. The last phenomenon, only emerged after introducing a more realistic synaptic input to the inhibitory interneurons. Two timescales were present in the interspike distributions. The behavior of one timescale was reproduced with different input balances received form the excitatory neurons, whereas the other, which disappears with cooling, could not be found assuming poissonian synaptic inputs. Furthermore, the computational model shows that the bursting of the excitatory neurons arises naturally at normal brain temperature and that they have an intrinsic delay at low temperatures. The same effect occurs at single synapses, which may explain song stretching. These findings shed light on the temperature dependence of neuronal dynamics and present a comprehensive framework to study neuronal connectivity. This study, which is based on intrinsic neuronal characteristics, may help to understand emergent behavioral changes.