Reconstruction of physiological instructions from Zebra finch song

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.

Neurally driven synthesis of learned, complex vocalizations

Brain machine interfaces (BMIs) hold promise to restore impaired motor function and serve as powerful tools to study learned motor skill. While limb-based motor prosthetic systems have leveraged nonhuman primates as an important animal model,^1-4 speech prostheses lack a similar animal model and are more limited in terms of neural interface technology, brain coverage, and behavioral study design.^5-7 Songbirds are an attractive model for learned complex vocal behavior. Birdsong shares a number of unique similarities with human speech,^8-10 and its study has yielded general insight into multiple mechanisms and circuits behind learning, execution, and maintenance of vocal motor skill.^11-18 In addition, the biomechanics of song production bear similarity to those of humans and some nonhuman primates.^19-23 Here, we demonstrate a vocal synthesizer for birdsong, realized by mapping neural population activity recorded from electrode arrays implanted in the premotor nucleus HVC onto low-dimensional compressed representations of song, using simple computational methods that are implementable in real time. Using a generative biomechanical model of the vocal organ (syrinx) as the low-dimensional target for these mappings allows for the synthesis of vocalizations that match the bird's own song. These results provide proof of concept that high-dimensional, complex natural behaviors can be directly synthesized from ongoing neural activity. This may inspire similar approaches to prosthetics in other species by exploiting knowledge of the peripheral systems and the temporal structure of their output.

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.

Multifunctional bilateral muscle control of vocal output in the songbird syrinx

Songbirds produce complex vocalizations by coordinating neuromuscular control of syrinx, respiratory system, and upper vocal tract. The functional roles of syringeal muscles have been documented mainly with correlative data, which have suggested that synergistic activation plays a role in the fine control of vocal features. However, the specific involvement of individual muscles in achieving this fine control is still largely unknown. Here we investigate the contributions of the two main airflow controlling muscles, the dorsal and ventral tracheobronchial muscles in the zebra finch, through a new approach. Ablation of the muscle insertion on the cartilage framework reveals detailed insights into their respective roles in the fine control of song features. Unilateral ablation of a tracheobronchial muscle resulted in mostly subtle changes of the air sac pressure pattern and song features. Effects of ablation varied with the acoustic elements, thus indicating a context-dependent specific synergistic activation of muscles. High-frequency notes were most affected by the ablation, highlighting the importance of coordinated bilateral control. More pronounced effects on song features and air sac pressure were observed after bilateral ablation of the dorsal tracheobronchial muscles. The results illustrate that the gating muscles serve multiple functions in control of acoustic features and that each feature arises through context-dependent, synergistic activation patterns of syringeal muscles. Although many changes after the ablation are subtle, they fall within the perceptual range and thus may control behaviorally relevant features of sound. These data therefore provide important specific details about the underlying motor code for song production.NEW & NOTEWORTHY A new experimental approach was used to analyze the involvement of individual muscles in birdsong vocal control. Ablation of tracheobronchial muscles showed how these muscles contribute in manner specific to the acoustic structure of sound segments and how disruption of airflow regulation affects bilateral coordination. The results of this study illustrate that the gating muscles serve multiple functions in control of acoustic features and give further insight into the complex motor control of birdsong.

Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires

Animals produce vocalizations that range in complexity from a single repeated call to hundreds of unique vocal elements patterned in sequences unfolding over hours. Characterizing complex vocalizations can require considerable effort and a deep intuition about each species' vocal behavior. Even with a great deal of experience, human characterizations of animal communication can be affected by human perceptual biases. We present a set of computational methods for projecting animal vocalizations into low dimensional latent representational spaces that are directly learned from the spectrograms of vocal signals. We apply these methods to diverse datasets from over 20 species, including humans, bats, songbirds, mice, cetaceans, and nonhuman primates. Latent projections uncover complex features of data in visually intuitive and quantifiable ways, enabling high-powered comparative analyses of vocal acoustics. We introduce methods for analyzing vocalizations as both discrete sequences and as continuous latent variables. Each method can be used to disentangle complex spectro-temporal structure and observe long-timescale organization in communication.

The evolution of the pectoral extrinsic appendicular and infrahyoid musculature in theropods and its functional and behavioral importance

Birds have lost and modified the musculature joining the pectoral girdle to the skull and hyoid, called the pectoral extrinsic appendicular and infrahyoid musculature. These muscles include the levator scapulae, sternomandibularis, sternohyoideus, episternocleidomastoideus, trapezius, and omohyoideus. As non-avian theropod dinosaurs are the closest relatives to birds, it is worth investigating what conditions they may have exhibited to learn when and how these muscles were lost or modified. Using extant phylogenetic bracketing, osteological correlates and non-osteological influences of these muscles are identified and discussed. Compsognathids and basal Maniraptoriformes were found to have been the likeliest transition points of a derived avian condition of losing or modifying these muscles. Increasing needs to control the feather tracts of the neck and shoulder, for insulation, display, or tightening/readjustment of the skin after dynamic neck movements may have been the selective force that drove some of these muscles to be modified into dermo-osseous muscles. The loss and modification of shoulder protractors created a more immobile girdle that would later be advantageous for flight in birds. The loss of the infrahyoid muscles freed the hyolarynx, trachea, and esophagus which may have aided in vocal tract filtering.

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.

Bayesian Filtering with Multiple Internal Models: Toward a Theory of Social Intelligence

To exhibit social intelligence, animals have to recognize whom they are communicating with. One way to make this inference is to select among internal generative models of each conspecific who may be encountered. However, these models also have to be learned via some form of Bayesian belief updating. This induces an interesting problem: When receiving sensory input generated by a particular conspecific, how does an animal know which internal model to update? We consider a theoretical and neurobiologically plausible solution that enables inference and learning of the processes that generate sensory inputs (e.g., listening and understanding) and reproduction of those inputs (e.g., talking or singing), under multiple generative models. This is based on recent advances in theoretical neurobiology-namely, active inference and post hoc (online) Bayesian model selection. In brief, this scheme fits sensory inputs under each generative model. Model parameters are then updated in proportion to the probability that each model could have generated the input (i.e., model evidence). The proposed scheme is demonstrated using a series of (real zebra finch) birdsongs, where each song is generated by several different birds. The scheme is implemented using physiologically plausible models of birdsong production. We show that generalized Bayesian filtering, combined with model selection, leads to successful learning across generative models, each possessing different parameters. These results highlight the utility of having multiple internal models when making inferences in social environments with multiple sources of sensory information.

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.

Neural coding of sound envelope structure in songbirds

Songbirds are a well-established animal model to study the neural basis of learning, perception and production of complex vocalizations. In this system, telencephalic neurons in HVC present a state-dependent, highly selective response to auditory presentations of the bird's own song (BOS). This property provides an opportunity to study the neural code behind a complex motor behavior. In this work, we explore whether changes in the temporal structure of the sound envelope can drive changes in the neural responses of highly selective HVC units. We generated an envelope-modified BOS (MOD) by reversing each syllable's envelope but leaving the overall temporal structure of syllable spectra unchanged, which resulted in a subtle modification for each song syllable. We conducted in vivo electrophysiological recordings of HVC neurons in anaesthetized zebra finches (Taeniopygia guttata). Units analyzed presented a high BOS selectivity and lower response to MOD, but preserved the profile response shape. These results show that the temporal evolution of the sound envelope is being sensed by the avian song system and suggest that the biomechanical properties of the vocal apparatus could play a role in enhancing subtle sound differences.

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.

Controllable biomimetic birdsong

Birdsong is the product of the controlled generation of sound embodied in a neuromotor system. From a biophysical perspective, a natural question is that of the difficulty of producing birdsong. To address this, we built a biomimetic syrinx consisting of a stretched simple rubber tube through which air is blown, subject to localized mechanical squeezing with a linear actuator. A large static tension on the tube and small dynamic variations in the localized squeezing allow us to control transitions between three states: a quiescent state, a periodic state and a solitary wave state. The static load brings the system close to threshold for spontaneous oscillations, while small dynamic loads allow for rapid transitions between the states. We use this to mimic a variety of birdsongs via the slow-fast modulated nonlinear dynamics of the physical substrate, the syrinx, regulated by a simple controller. Finally, a minimal mathematical model of the system inspired by our observations allows us to address the problem of song mimicry in an excitable oscillator for tonal songs.

Population-Level Representation of a Temporal Sequence Underlying Song Production in the Zebra Finch

The zebra finch brain features a set of clearly defined and hierarchically arranged motor nuclei that are selectively responsible for producing singing behavior. One of these regions, a critical forebrain structure called HVC, contains premotor neurons that are active at precise time points during song production. However, the neural representation of this behavior at a population level remains elusive. We used two-photon microscopy to monitor ensemble activity during singing, integrating across multiple trials by adopting a Bayesian inference approach to more precisely estimate burst timing. Additionally, we examined spiking and motor-related synaptic inputs using intracellular recordings during singing. With both experimental approaches, we find that premotor events do not occur preferentially at the onsets or offsets of song syllables or at specific subsyllabic motor landmarks. These results strongly support the notion that HVC projection neurons collectively exhibit a temporal sequence during singing that is uncoupled from ongoing movements.

Rhythmic Continuous-Time Coding in the Songbird Analog of Vocal Motor Cortex

Songbirds learn and produce complex sequences of vocal gestures. Adult birdsong requires premotor nucleus HVC, in which projection neurons (PNs) burst sparsely at stereotyped times in the song. It has been hypothesized that PN bursts, as a population, form a continuous sequence, while a different model of HVC function proposes that both HVC PN and interneuron activity is tightly organized around motor gestures. Using a large dataset of PNs and interneurons recorded in singing birds, we test several predictions of these models. We find that PN bursts in adult birds are continuously and nearly uniformly distributed throughout song. However, we also find that PN and interneuron firing rates exhibit significant 10-Hz rhythmicity locked to song syllables, peaking prior to syllable onsets and suppressed prior to offsets-a pattern that predominates PN and interneuron activity in HVC during early stages of vocal learning.

From perception to action in songbird production: dynamics of a whole loop

Birdsong emerges when a set of highly interconnected brain areas manage to generate a complex output. This consists of precise respiratory rhythms as well as motor instructions to control the vocal organ configuration. In this way, during birdsong production, dedicated cortical areas interact with life-supporting ones in the brainstem, such as the respiratory nuclei. We discuss an integrative view of this interaction together with a widely accepted "top-down" representation of the song system. We also show that a description of this neural network in terms of dynamical systems allows to explore songbird production and processing by generating testable predictions.

Rhythmic syllable-related activity in a songbird motor thalamic nucleus necessary for learned vocalizations

Birdsong is a complex behavior that exhibits hierarchical organization. While the representation of singing behavior and its hierarchical organization has been studied in some detail in avian cortical premotor circuits, our understanding of the role of the thalamus in adult birdsong is incomplete. Using a combination of behavioral and electrophysiological studies, we seek to expand on earlier work showing that the thalamic nucleus Uvaeformis (Uva) is necessary for the production of stereotyped, adult song in zebra finch (Taeniopygia guttata). We confirm that complete bilateral lesions of Uva abolish singing in the ‘directed’ social context, but find that in the ‘undirected’ social context, such lesions result in highly variable vocalizations similar to early babbling song in juvenile birds. Recordings of neural activity in Uva reveal strong syllable-related modulation, maximally active prior to syllable onsets and minimally active prior to syllable offsets. Furthermore, both song and Uva activity exhibit a pronounced coherent modulation at 10Hz—a pattern observed in downstream premotor areas in adult and, even more prominently, in juvenile birds. These findings are broadly consistent with the idea that Uva is critical in the sequential activation of behavioral modules in HVC.

Vocal development in a Waddington landscape

Vocal development is the adaptive coordination of the vocal apparatus, muscles, the nervous system, and social interaction. Here, we use a quantitative framework based on optimal control theory and Waddington’s landscape metaphor to provide an integrated view of this process. With a biomechanical model of the marmoset monkey vocal apparatus and behavioral developmental data, we show that only the combination of the developing vocal tract, vocal apparatus muscles and nervous system can fully account for the patterns of vocal development. Together, these elements influence the shape of the monkeys’ vocal developmental landscape, tilting, rotating or shifting it in different ways. We can thus use this framework to make quantitative predictions regarding how interfering factors or experimental perturbations can change the landscape within a species, or to explain comparative differences in vocal development across species

DOI:http://dx.doi.org/10.7554/eLife.20782.001

As infants develop they learn new behaviors and refine existing ones. For example, human infants progress from crying to babbling to producing speech-like sounds. A complex sequence of changes in muscles, the nervous system and in patterns of interactions with other individuals all contribute to these emerging behaviors.

Despite this complexity, most studies of vocal development have only considered single factors in isolation. A study of speech development, for example, might examine how changes in the brain enable infants to imitate sounds. However, that same study will probably ignore how changes in the structure of the vocal cords, or in the behavior of the parents, also promote imitation.

Young marmoset monkeys, like human infants, gradually develop from producing immature cries to adult-like calls. Teramoto, Takahashi et al. built a computational model of this process and compared the model to data from real animals. The first version of the model focused solely on how the marmosets’ vocal cords grow, and did not fully reproduce how adult-like calls emerge in real marmosets. Teramoto, Takahashi et al. therefore added factors to the model that simulate improvements in muscle control, learning in the nervous system and in the behavior of other animals. These findings show that, to reflect how adult-like calls emerge in real marmosets, the model needs to include all of these factors.

The model developed by Teramoto, Takahashi et al. may also provide insights into why vocal learning and some other behaviors emerge in some species and not others. It may also be used to predict the consequences of disrupting individual processes in young animals at particular points in time and how such disruptions shape the way an animal develops on its way to adulthood.

DOI:http://dx.doi.org/10.7554/eLife.20782.002

Automatic reconstruction of physiological gestures used in a model of birdsong production

Highly coordinated learned behaviors are key to understanding neural processes integrating the body and the environment. Birdsong production is a widely studied example of such behavior in which numerous thoracic muscles control respiratory inspiration and expiration: the muscles of the syrinx control syringeal membrane tension, while upper vocal tract morphology controls resonances that modulate the vocal system output. All these muscles have to be coordinated in precise sequences to generate the elaborate vocalizations that characterize an individual's song. Previously we used a low-dimensional description of the biomechanics of birdsong production to investigate the associated neural codes, an approach that complements traditional spectrographic analysis. The prior study used algorithmic yet manual procedures to model singing behavior. In the present work, we present an automatic procedure to extract low-dimensional motor gestures that could predict vocal behavior. We recorded zebra finch songs and generated synthetic copies automatically, using a biomechanical model for the vocal apparatus and vocal tract. This dynamical model described song as a sequence of physiological parameters the birds control during singing. To validate this procedure, we recorded electrophysiological activity of the telencephalic nucleus HVC. HVC neurons were highly selective to the auditory presentation of the bird's own song (BOS) and gave similar selective responses to the automatically generated synthetic model of song (AUTO). Our results demonstrate meaningful dimensionality reduction in terms of physiological parameters that individual birds could actually control. Furthermore, this methodology can be extended to other vocal systems to study fine motor control.

Parameter estimation, nonlinearity, and Occam's razor

Nonlinear systems are capable of displaying complex behavior even if this is the result of a small number of interacting time scales. A widely studied case is when complex dynamics emerges out of a nonlinear system being forced by a simple harmonic function. In order to identify if a recorded time series is the result of a nonlinear system responding to a simpler forcing, we develop a discrete nonlinear transformation for time series based on synchronization techniques. This allows a parameter estimation procedure which simultaneously searches for a good fit of the recorded data, and small complexity of a fluctuating driving parameter. We illustrate this procedure using data from respiratory patterns during birdsong production.

Modeling vocalization with ECoG cortical activity recorded during vocal production in the macaque monkey

Vocal production is an example of controlled motor behavior with high temporal precision. Previous studies have decoded auditory evoked cortical activity while monkeys listened to vocalization sounds. On the other hand, there have been few attempts at decoding motor cortical activity during vocal production. Here we recorded cortical activity during vocal production in the macaque with a chronically implanted electrocorticographic (ECoG) electrode array. The array detected robust activity in motor cortex during vocal production. We used a nonlinear dynamical model of the vocal organ to reduce the dimensionality of `Coo' calls produced by the monkey. We then used linear regression to evaluate the information in motor cortical activity for this reduced representation of calls. This simple linear model accounted for circa 65% of the variance in the reduced sound representations, supporting the feasibility of using the dynamical model of the vocal organ for decoding motor cortical activity during vocal production.

The singer and the song: the neuromechanics of avian sound production

Song is crucial to songbirds for establishing territories and signaling genetic quality and an important driver in speciation. Songbirds also have become a widely used experimental model system to study the neural basis of vocal learning, a form of imitation learning with strong parallels to human speech learning. While there is a strong focus on central processing of song production, we still have limited insights into the functional output of the motor neural circuits. This review focuses on recent developments in motor control, biomechanics and feedback mechanisms of sound production in songbirds.

Evidence and control of bifurcations in a respiratory system

We studied the pressure patterns used by domestic canaries in the production of birdsong. Acoustically different sound elements ("syllables") were generated by qualitatively different pressure gestures. We found that some ubiquitous transitions between syllables can be interpreted as bifurcations of a low dimensional dynamical system. We interpreted these results as evidence supporting a model in which different timescales interact nonlinearly.

A mechanism for frequency modulation in songbirds shared with humans

In most animals that vocalize, control of fundamental frequency is a key element for effective communication. In humans, subglottal pressure controls vocal intensity but also influences fundamental frequency during phonation. Given the underlying similarities in the biomechanical mechanisms of vocalization in humans and songbirds, songbirds offer an attractive opportunity to study frequency modulation by pressure. Here, we present a novel technique for dynamic control of subsyringeal pressure in zebra finches. By regulating the opening of a custom-built fast valve connected to the air sac system, we achieved partial or total silencing of specific syllables, and could modify syllabic acoustics through more complex manipulations of air sac pressure. We also observed that more nuanced pressure variations over a limited interval during production of a syllable concomitantly affected the frequency of that syllable segment. These results can be explained in terms of a mathematical model for phonation that incorporates a nonlinear description for the vocal source capable of generating the observed frequency modulations induced by pressure variations. We conclude that the observed interaction between pressure and frequency was a feature of the source, not a result of feedback control. Our results indicate that, beyond regulating phonation or its absence, regulation of pressure is important for control of fundamental frequencies of vocalizations. Thus, although there are separate brainstem pathways for syringeal and respiratory control of song production, both can affect airflow and frequency. We hypothesize that the control of pressure and frequency is combined holistically at higher levels of the vocalization pathways.

The Physics of Birdsong Production

Human babies need to learn how to talk. The need of a tutor to achieve acceptable vocalizations is a feature that we share with a few species in the animal kingdom. Among those are Songbirds, which account for nearly half of the known bird species. For that reason, Songbirds have become an ideal animal model to study how a brain reconfigures itself during the process of learning a complex task. In the last years, neuroscientists have invested important resources in order to unveil the neural architecture involved in birdsong production and learning. Yet, behavior emerges from the interaction between a nervous system, a peripheral biomechanical architecture and environment, and therefore its study should be just as integrated. In particular, the physical study of the avian vocal organ can help to elucidate which features found in the song of birds are under direct control of specific neural instructions and which emerge from the biomechanics involved in its generation. This work describes recent advances in the study of the physics of birdsong production.

Elemental gesture dynamics are encoded by song premotor cortical neurons

Quantitative biomechanical models can identify control parameters used during movements, and movement parameters encoded by premotor neurons. We fit a mathematical dynamical systems model including subsyringeal pressure, syringeal biomechanics, and upper vocal tract filtering to the songs of zebra finches. This reduced the dimensionality of singing dynamics, described as trajectories in pressure-tension space (motor “gestures”). We assessed model performance by characterizing the auditory response "replay" of song premotor HVC neurons to presentation of song variants in sleeping birds, and by examining HVC activity in singing birds. HVC projection neurons were excited and interneurons were suppressed with near-zero time lag, at times of gesture trajectory extrema. Thus, HVC precisely encodes vocal motor output via the timing of extreme points of movement trajectories. We propose that the sequential activity of HVC neurons represents the sequence of gestures in song as a “forward” model making predictions on expected behavior to evaluate feedback.

The acoustic effect of vocal tract adjustments in zebra finches

Vocal production in songbirds requires the control of the respiratory system, the syrinx as sound source and the vocal tract as acoustic filter. Vocal tract movements consist of beak, tongue and hyoid movements, which change the volume of the oropharyngeal-esophageal cavity (OEC), glottal movements and tracheal length changes. The respective contributions of each movement to filter properties are not completely understood, but the effects of this filtering are thought to be very important for acoustic communication in birds. One of the most striking movements of the upper vocal tract during vocal behavior in songbirds involves the OEC. This study measured the acoustic effect of OEC adjustments in zebra finches by comparing resonance acoustics between an utterance with OEC expansion (calls) and a similar utterance without OEC expansion (respiratory sounds induced by a bilateral syringeal denervation). X-ray cineradiography confirmed the presence of an OEC motor pattern during song and call production, and a custom-built Hall-effect collar system confirmed that OEC expansion movements were not present during respiratory sounds. The spectral emphasis during zebra finch call production ranging between 2.5 and 5 kHz was not present during respiratory sounds, indicating strongly that it can be attributed to the OEC expansion.

Perception and self-organized instability

This paper considers state-dependent dynamics that mediate perception in the brain. In particular, it considers the formal basis of self-organized instabilities that enable perceptual transitions during Bayes-optimal perception. The basic phenomena we consider are perceptual transitions that lead to conscious ignition (Dehaene and Changeux, 2011) and how they depend on dynamical instabilities that underlie chaotic itinerancy (Breakspear, 2001; Tsuda, 2001) and self-organized criticality (Beggs and Plenz, 2003; Plenz and Thiagarajan, 2007; Shew et al., 2011). Our approach is based on a dynamical formulation of perception as approximate Bayesian inference, in terms of variational free energy minimization. This formulation suggests that perception has an inherent tendency to induce dynamical instabilities (critical slowing) that enable the brain to respond sensitively to sensory perturbations. We briefly review the dynamics of perception, in terms of generalized Bayesian filtering and free energy minimization, present a formal conjecture about self-organized instability and then test this conjecture, using neuronal (numerical) simulations of perceptual categorization.

Prosthetic avian vocal organ controlled by a freely behaving bird based on a low dimensional model of the biomechanical periphery

Because of the parallels found with human language production and acquisition, birdsong is an ideal animal model to study general mechanisms underlying complex, learned motor behavior. The rich and diverse vocalizations of songbirds emerge as a result of the interaction between a pattern generator in the brain and a highly nontrivial nonlinear periphery. Much of the complexity of this vocal behavior has been understood by studying the physics of the avian vocal organ, particularly the syrinx. A mathematical model describing the complex periphery as a nonlinear dynamical system leads to the conclusion that nontrivial behavior emerges even when the organ is commanded by simple motor instructions: smooth paths in a low dimensional parameter space. An analysis of the model provides insight into which parameters are responsible for generating a rich variety of diverse vocalizations, and what the physiological meaning of these parameters is. By recording the physiological motor instructions elicited by a spontaneously singing muted bird and computing the model on a Digital Signal Processor in real-time, we produce realistic synthetic vocalizations that replace the bird's own auditory feedback. In this way, we build a bio-prosthetic avian vocal organ driven by a freely behaving bird via its physiologically coded motor commands. Since it is based on a low-dimensional nonlinear mathematical model of the peripheral effector, the emulation of the motor behavior requires light computation, in such a way that our bio-prosthetic device can be implemented on a portable platform.

Brain Machine Interfaces (BMIs) decode motor instructions from neuro-physiological recordings and feed them to bio-mimetic effectors. Many applications achieve high accuracy on a limited number of tasks by applying statistical methods to these data to extract features corresponding to certain motor instructions. We built a bio-prosthetic avian vocal organ. The device is based on a low-dimensional mathematical model that accounts for the dynamics of the bird's vocal organ and robustly relates smooth paths in a physiologically meaningful parameter space to complex sequences of vocalizations. The two physiological motor gestures (sub-syringeal pressure and ventral syringeal muscular activity), are reconstructed from the bird's song, and the model is implemented on a portable Digital Signal Processor to produce synthetic birdsong when driven by a freely behaving bird via the sub-syringeal pressure gesture. This exemplifies the plausibility of a type of synthetic interfacing between the brain and a complex behavior. In this type of devices, the understanding of the bio-mechanics of the periphery is key to identifying a low dimensional physiological signal coding the motor instructions, therefore enabling real-time implementation at a low computational cost.