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

pre-printBecause 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

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

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

Illustration of the parameter fitting and calibration procedure.

<p>The thoracic air sac pressure is recorded together with the song; the pressure and corresponding sound of a bout are shown in (A). With these records, the temporal series that originate the syllables corresponding to the motif are constructed (B). After muting the bird and registering the pressure gesture as it attempts to produce a motif, the detection algorithm is tuned. In (C) we show the degraded pressure gesture, together with the correlation with the chosen segment of the intact pressure gesture. The segments pointed out by arrows indicate the detection of the intention to sing a motif. Song is synthesized during these periods by integration of the model with the parameters found previously. In (D), we show the pressure gesture of the muted bird and the output of the trigger/integrate algorithm.</p

Intact song and subject-driven, synthetic song.

<p>Intact pressure gesture and sonogram, with different colors and opacity of shading indicating the different syllables, and an arrow indicating the segment of the first syllable used for detection (upper panels). When the muted bird drives the syrinx, we see in the sonogram that synthetic sound is produced after the first syllable is detected and until recognition of the interruption of the motif (lower panels).</p

Undirected singing rate as a non-invasive tool for welfare monitoring in isolated male zebra finches

Research on the songbird zebra finch (Taeniopygia guttata) has advanced our behavioral, hormonal, neuronal, and genetic understanding of vocal learning. However, little is known about the impact of typical experimental manipulations on the welfare of these birds. Here we explore whether the undirected singing rate can be used as an indicator of welfare. We tested this idea by performing a post hoc analysis of singing behavior in isolated male zebra finches subjected to interactive white noise, to surgery, or to tethering. We find that the latter two experimental manipulations transiently but reliably decreased singing rates. By contraposition, we infer that a high-sustained singing rate is suggestive of successful coping or improved welfare in these experiments. Our analysis across more than 300 days of song data suggests that a singing rate above a threshold of several hundred song motifs per day implies an absence of an acute stressor or a successful coping with stress. Because singing rate can be measured in a completely automatic fashion, its observation can help to reduce experimenter bias in welfare monitoring. Because singing rate measurements are non-invasive, we expect this study to contribute to the refinement of the current welfare monitoring tools in zebra finches

Stimulus dependent diversity and stereotypy in the output of an olfactory functional unit

Olfactory inputs are organized in an array of functional units (glomeruli), each relaying information from sensory neurons expressing a given odorant receptor to a small population of output neurons, mitral/tufted (MT) cells. MT cells respond heterogeneously to odorants, and how the responses encode stimulus features is unknown. We recorded in awake mice responses from "sister" MT cells that receive input from a functionally characterized, genetically identified glomerulus, corresponding to a specific receptor (M72). Despite receiving similar inputs, sister MT cells exhibit temporally diverse, concentration-dependent, excitatory and inhibitory responses to most M72 ligands. In contrast, the strongest known ligand for M72 elicits temporally stereotyped, early excitatory responses in sister MT cells, consistent across a range of concentrations. Our data suggest that information about ligand affinity is encoded in the collective stereotypy or diversity of activity among sister MT cells within a glomerular functional unit in a concentration-tolerant manner