We reconstruct the physiological parameters that control an avian vocal organ during birdsong production using recorded song. The procedure involves fitting the time dependent parameters of an avian vocal organ model. Computationally, the model is implemented as a dynamical system ruling the behavior of the oscillating labia that modulate the air flow during sound production, together with the equations describing the dynamics of pressure fluctuations in the vocal tract. We tested our procedure for Zebra finch song with, simultaneously recorded physiological parameters: air sac pressure and the electromyographic activity of the left and right ventral syringeal muscles. A comparison of the reconstructed instructions with measured physiological parameters during song shows a high degree of correlation. Integrating the model with reconstructed parameters leads to the synthesis of highly realistic songs.

Detailed information regarding the alloy deposition/ dealloying and fabrication steps, the energy dispersive X-ray spectral characterization, histology on chronically implanted mice and characterization of explanted electrodes, electrochemical impedance spectroscopy and their small signal components, sterilization effects of autoclave, ethylene oxide, and sterrad, on impedance distribution, comparison of surface and depth recorded single units and extracted composite receptive fields in songbird experiments, and comparison of recordings using PtNR devices and NeuroNexus ECoG Pt electrodes on NHP and corresponding power-frequency plots (PDF)

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.

Birdsong is a complex phenomenon, generated by a nonlinear vocal device capable of displaying complex solutions even under simple physiological motor commands. Among the peripheral physical mechanisms responsible for the generation of complex sounds in songbirds, the understanding of the dynamics emerging from the interaction between the sound source and the upper vocal tract remains most elusive. In this work we study a highly dissipative limit of a simple sound source model interacting with a tract, mathematically described in terms of a delay differential equation. We explore the system numerically and, by means of reducing the problem to a phase equation, we are capable of studying its periodic solutions. Close in parameter space to the point where the resonances of the tract match the frequencies of the uncoupled source solutions, we find coexistence of periodic limit cycles. This hysteresis phenomenon allows us to interpret recently reported features found in the vocalization of some songbirds, in particular, "frequency jumps."

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.

In this work, we build an electronic syrinx, i.e., a programmable electronic device capable of integrating biomechanical model equations for the avian vocal organ in order to synthesize song. This vocal prosthesis is controlled by the bird's neural instructions to respiratory and the syringeal motor systems, thus opening great potential for studying motor control and its modification by sensory feedback mechanisms. Furthermore, a well-functioning subject-controlled vocal prosthesis can lay the foundation for similar devices in humans and thus provide directly health-related data and procedures. DOI: 10.1103/PhysRevE.81.031927 PACS number͑s͒: 87.19.lu, 87.10.Ed Complex motor behavior emerges from the interactions between a nervous system and peripheral effectors systems ͓1͔. This interplay is clearly illustrated in birdsong production, where a highly nonlinear device is capable of generating a variety of acoustically different sounds, even when driven by relatively simple physiological instructions ͓2,3͔. Recently, the modeling of the avian vocal organ has helped to understand the relationships between different acoustic features, which are not under direct neural control but are determined by the biomechanics of the peripheral system ͓2,3͔. In this work, we implement an electronic device which continuously reads physiological instructions driving the syrinx and integrates the model equations ruling its dynamics in the time lapse between readings.The most widely studied songbird species is the zebra finch ͑Taeniopygia guttata͒, whose song consists of three to eight distinct song syllables with a variety of acoustic characteristics. Whereas many songbird species produce sounds with low upper harmonic content ͑tonal͒, zebra finch song is composed of both spectrally rich and tonal syllables. In a previous work, we found that there is a relationship between the spectral content of a vocalization and its fundamental frequency ͓2͔. Moreover, this relationship can be explained in terms of the different dynamical mechanisms by which labial oscillations are started when the air sac pressure reaches a threshold value ͓Hopf mechanism versus a saddle node in a limit cycle ͑SNILC͒ bifurcation; e.g., ͓4͔͔. These different mechanisms were found in a physical model for birdsong production ͓2,3͔, which illustrates that some acoustic features of the song are not under direct control of the nervous system but emerge from the interactions with the biomechanical device.One of the first low-dimensional models for the dynamics of a membrane in an airflow was proposed by Titze and Martin ͓5͔ and has been used to describe the source in birdsong ͓6͔. The model assumes that for high-enough values of the airflow, soft pieces of tissue ͑labia, in the case of birds͒ start to oscillate. The modulations of the airflow are the responsible for the sound. The motion of the oscillating tissues, in this model, is represented as a surface wave propagating in the direction of the airflow. In order to describe this wave, Titze assumed two basic mod...

Olfactory inputs are organized in an array of parallel functional units (glomeruli), each relaying information from sensory neurons that express a given odorant receptor to a small population of output neurons, mitral/tufted (MT) cells. MT cells have complex temporal responses to odorants, but how these diverse responses relate to stimulus features is not known. 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 exhibited temporally diverse, concentration variant, excitatory and inhibitory responses to most M72 ligands. In contrast, the strongest known ligand for M72 elicited temporallystereotyped, early excitatory responses in all sister MT cells that persisted across all odor concentrations. Our data demonstrate 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 concentration-independent manner.

High-fidelity measurements of neural activity can enable advancements in our understanding of the neural basis of complex behaviors such as speech, audition, and language, and are critical for developing neural prostheses that address impairments to these abilities due to disease or injury. We develop a novel high resolution, thin-film micro-electrocorticography (micro-ECoG) array that enables highfidelity surface measurements of neural activity from songbirds, a well-established animal model for studying speech behavior. With this device, we provide the first demonstration of sensory-evoked modulation of surface-recorded single unit responses. We establish that single unit activity is consistently sensed from micro-ECoG electrodes over the surface of sensorimotor nucleus HVC (used as a proper name) in anesthetized European starlings, and validate responses with correlated firing in single units recorded simultaneously at surface and depth. The results establish a platform for high-fidelity recording from the surface of subcortical structures that will accelerate neurophysiological studies, and development of novel electrode arrays and neural prostheses.