Biology subjects

Gentner, T. Q.

Publications and source records attributed to Gentner, T. Q..

10 recordsLinked to original sources

bioRxiv2026

Automated inference of respiratory and syringeal biomechanical trajectories from birdsong acoustics

Songbirds, in particular zebra finches (Taeniopygia guttata), provide a powerful model for investigating the neural mechanisms of learned vocal behavior. Researchers typically rely on the acoustic structure of birdsong to quantify vocal behavior. As a more direct measure of motor control, we present VIBE: Vocal acoustic Inversion to Biomechanical Estimates, an open-source pipeline that recovers the biomechanical control parameters of song production directly from the acoustic waveform. Biomechanical models of the songbird syrinx describe vocal production with two continuously varying parameters: and {beta}, representing subsyringeal air sac pressure and syringeal muscle tension, respectively. Recovering these parameters from song acoustics provides a motor-based coordinate system against which neural activity or other dependent variables can be directly compared. Because and {beta} are the coupled control parameters of a nonlinear oscillator, their joint recovery is non-trivial. VIBE addresses this through iterative optimization of the governing normal-form equations. We validate VIBE against recorded air sac pressure across 44 songs from twelve birds, showing that the recovered corresponds to empirically measured air sac pressure. Pairing VIBE with Neuropixels recordings from RA in five birds, we find that RA activity is well predicted by the recovered parameters, and that and {beta} add predictive power beyond the acoustic features of song. By recovering biomechanical control parameters from the acoustic signal, VIBE makes the biomechanical coordinate system of song production accessible to the broader songbird research community. New & NoteworthyVIBE provides a novel, fully automated pipeline to recover the biomechanical control parameters of the avian vocal organ, and {beta}, as continuously varying quantities from the raw acoustic waveform, making the full biomechanical model of song production accessible at the scale of modern datasets.

neuroscience↗

bioRxiv2026

Anticipatory organization of neural population dynamics speeds behavioral decisions

Expectations guide behavior and shape sensory responses in single neurons, but their influence on population-level neural dynamics is unknown. Here, we employ a dynamical systems framework to examine the collective spiking activity of neuronal populations in the auditory forebrain of European starlings, a species of songbird, as they categorize natural song syllables while sensory expectations are manipulated. We show first that sensory-driven neural population spiking activity traces smooth, low-dimensional latent trajectories that closely reflect the identity of sensory signals. Like the stimulus-driven responses of single neurons, the geometry of the population trajectories is also modulated by expectation. In single neurons, expectation sharpens differences between responses to signals in the same category, but at the population-level the effect is opposite: expectation increases the similarity between responses to signals in the same category. To understand how population-level response dynamics can differ from those in single neurons, we develop (and test empirically) a dynamical model that relates spiking activity at these two biological scales. The model leverages response redundancy between neurons, a capacity we term degeneracy-enabled remapping, and enables the observed simultaneous expectation-dependent increases in the separability of single-neuron responses and decreases in the separability of population trajectories in the task-potent subspace, i.e., the population activity dimensions tied to behavioral categorization. Examining the relationship between expectation-modulated population trajectories and behavior in detail, we find that single-trial categorization errors are tied to drift in the trajectory toward the opposing task-potent manifold. This suggests that expectations help establish structured, hypothesis-dependent initial conditions that precede the target-driven population response. In support of this, both behavioral accuracy and behavioral reaction time are predicted by the direction of early population motion within the task-potent subspace. We conclude that expectation drives anticipatory organization of population response variability into a structured, behaviorally relevant geometry that pre-positions subsequent population activity on task-potent manifolds to support rapid, accurate, behavioral outcomes.

neuroscience↗

bioRxiv2024

Few-shot Algorithms for Consistent Neural Decoding (FALCON) Benchmark

Intracortical brain-computer interfaces (iBCIs) can restore movement and communication abilities to individuals with paralysis by decoding their intended behavior from neural activity recorded with an implanted device. While this activity yields high-performance decoding over short timescales, neural data are often nonstationary, which can lead to decoder failure if not accounted for. To maintain performance, users must frequently recalibrate decoders, which requires the arduous collection of new neural and behavioral data. Aiming to reduce this burden, several approaches have been developed that either limit recalibration data requirements (few-shot approaches) or eliminate explicit recalibration entirely (zero-shot approaches). However, progress is limited by a lack of standardized datasets and comparison metrics, causing methods to be compared in an ad hoc manner. Here we introduce the FALCON benchmark suite (Few-shot Algorithms for COnsistent Neural decoding) to standardize evaluation of iBCI robustness. FALCON curates five datasets of neural and behavioral data that span movement and communication tasks to focus on behaviors of interest to modern-day iBCIs. Each dataset includes calibration data, optional few-shot recalibration data, and private evaluation data. We implement a flexible evaluation platform which only requires user-submitted code to return behavioral predictions on unseen data. We also seed the benchmark by applying baseline methods spanning several classes of possible approaches. FALCON aims to provide rigorous selection criteria for robust iBCI decoders, easing their translation to real-world devices. https://snel-repo.github.io/falcon/

neuroscience↗

bioRxiv2024

Auditory Feature-based Perceptual Distance

Studies comparing acoustic signals often rely on pixel-wise differences between spectrograms, as in for example mean squared error (MSE). Pixel-wise errors are not representative of perceptual sensitivity, however, and such measures can be highly sensitive to small local signal changes that may be imperceptible. In computer vision, high-level visual features extracted with convolutional neural networks (CNN) can be used to calculate the fidelity of computer-generated images. Here, we propose the auditory perceptual distance (APD) metric based on acoustic features extracted with an unsupervised CNN and validated by perceptual behavior. Using complex vocal signals from songbirds, we trained a Siamese CNN on a self-supervised task using spectrograms rescaled to match the auditory frequency sensitivity of European starlings, Sturnus vulgaris. We define APD for any pair of sounds as the cosine distance between corresponding feature vectors extracted by the trained CNN. We show that APD is more robust to temporal and spectral translation than MSE, and captures the sigmoidal shape of typical behavioral psychometric functions over complex acoustic spaces. When fine-tuned using starlings behavioral judgments of naturalistic song syllables, the APD model yields even more accurate predictions of perceptual sensitivity, discrimination, and categorization on novel complex (high-dimensional) acoustic dimensions, including diverging decisions for identical stimuli following different training conditions. Thus, the APD model outperforms MSE in robustness and perceptual accuracy, and offers tunability to match experience-dependent perceptual biases.

animal behavior and cognition↗

bioRxiv2024

Predictive coding for natural vocal signals in the songbird auditory forebrain

Predictive coding posits that sensory signals are compared to internal models, with resulting prediction-error carried in the spiking responses of single neurons. Despite its proposal as a general cortical mechanism, including for speech processing, whether or how predictive coding functions in single-neuron responses to vocal communication signals is unknown. As a proxy internal model, we developed a neural network that uses current sensory context to predict future spectrotemporal features of a vocal communication signal, birdsong. We then represent birdsong as either weighted sets of latent predictive features evolving in time, or as time-varying prediction-errors that reflect the difference between ongoing network-predicted and actual song. Using these spectrotemporal, predictive, and prediction-error song representations, we fit linear/non-linear receptive fields to single neuron responses recorded from caudomedial nidopallium (NCM), caudal mesopallium (CMM) and Field L, analogs of mammalian auditory cortices, in anesthetized European starlings, Sturnus vulgaris, listening to conspecific songs. In all three regions, the predictive features of song yield the single best model of song-evoked spiking responses, but unique information about all three representations (signal, prediction, and error) is carried in the spiking responses to song. The relative weighting of this information varies across regions, but in contrast to many computational predictive coding models neither predictive nor error responses are segregated in separate neurons. The continuous interplay between prediction and prediction-error is consistent with the relevance of predictive coding for cortical processing of temporally patterned vocal communication signals, but new models for how prediction and error are integrated in single neurons are required.

neuroscience↗

bioRxiv2022

Context-dependent sensory modulation underlies Bayesian vocal sequence perception

Vocal communication in both songbirds and humans relies on categorical perception of smoothly varying acoustic spaces. Vocal perception can be biased by expectation and context, but the mechanisms of this bias are not well understood. We developed a behavioral task in which songbirds, European starlings, are trained to to classify smoothly varying song syllables in the context of predictive syllable sequences. We find that syllable-sequence predictability biases perceptual categorization following a Bayesian model of probabilistic information integration. We then recorded from populations of neurons in the auditory forebrain while birds actively categorized song syllables, observing large proportions of neurons that track the smoothly varying natural feature space of syllable categories. We observe that predictive information in the syllable sequences dynamically modulates sensory neural representations. These results support a Bayesian model of perception where predictive information acts to dynamically reallocate sensory neural resources, sharpening acuity (i.e. the likelihood) in high-probability regions of stimulus space. One-Sentence SummaryPredictive information in vocal sequences biases Bayesian categorical perception through rapid sensory reorganization. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=156 SRC="FIGDIR/small/488412v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@748eecorg.highwire.dtl.DTLVardef@533792org.highwire.dtl.DTLVardef@1658eeeorg.highwire.dtl.DTLVardef@8ad501_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2020

Long-range sequential dependencies precede complex syntactic production in language acquisition

To convey meaning, human language relies on hierarchically organized, long-range relationships spanning words, phrases, sentences, and discourse. The strength of the relationships between sequentially ordered elements of language (e.g., phonemes, characters, words) decays following a power law as a function of sequential distance. To understand the origins of these relationships, we examined long-range statistical structure in the speech of human children at multiple developmental time points, along with non-linguistic behaviors in humans and phylogenetically distant species. Here we show that adult-like power-law statistical dependencies precede the production of hierarchically-organized linguistic structures, and thus cannot be driven solely by these structures. Moreover, we show that similar long-range relationships occur in diverse non-linguistic behaviors across species. We propose that the hierarchical organization of human language evolved to exploit pre-existing long-range structure present in much larger classes of non-linguistic behavior, and that the cognitive capacity to model long-range hierarchical relationships preceded language evolution. We call this the Statistical Scaffolding Hypothesis for language evolution. 1 Significance StatementHuman language is uniquely characterized by semantically meaningful hierarchical organization, conveying information over long timescales. At the same time, many non-linguistic human and animal behaviors are also often characterized by richly hierarchical organization. Here, we compare the long-timescale statistical dependencies present in language to those present in non-linguistic human and animal behaviors as well as language production throughout childhood. We find adult-like, long-timescale relationships early in language development, before syntax or complex semantics emerge, and we find similar relationships in non-linguistic behaviors like cooking and even housefly movement. These parallels demonstrate that long-range statistical dependencies are not unique to language and suggest a possible evolutionary substrate for the long-range hierarchical structure present in human language.

evolutionary biology↗

bioRxiv2020

Local Field Potentials in a Pre-motor Region Predict Learned Vocal Sequences

Neuronal activity within the premotor region HVC is tightly synchronized to, and crucial for, the articulate production of learned song in birds. Characterizations of this neural activity typically focuses on patterns of sequential bursting in small carefully identified subsets of single neurons in the HVC population. Much less is known about population dynamics beyond the scale of individual neurons. There is a rich history of using local field potentials (LFP), to extract information about behavior that extends beyond the contribution of individual cells. These signals have the advantage of being stable over longer periods of time and have been used to study and decode complex motor behaviors, such as human speech. Here we characterize LFP signals in the putative HVC of freely behaving male zebra finches during song production, to determine if population activity may yield similar insights into the mechanisms underlying complex motor-vocal behavior. Following an initial observation that structured changes in the LFP were distinct to all vocalizations during song, we show that it is possible to extract time varying features from multiple frequency bands to decode the identity of specific vocalization elements (syllables) and to predict their temporal onsets within the motif. This demonstrates that LFP is a useful signal for studying motor control in songbirds. Surprisingly, the time frequency structure of putative HVC LFP is qualitatively similar to well established oscillations found in both human and non-human mammalian motor areas. This physiological similarity, despite distinct anatomical structures, may give insight to common computational principles for learning and/or generating complex motor-vocal behaviors. Author SummaryVocalizations, such as speech and song, are a motor process that requires the coordination of several muscle groups receiving instructions from specific brain regions. In songbirds, HVC is a premotor brain region required for singing and it is populated by a set of neurons that fire sparsely during song. How HVC enables song generation is not well understood. Here we describe network activity in putative HVC that precedes the initiation of each vocal element during singing. This network activity can be used to predict both the identity of each vocal element (syllable) and when it will occur during song. In addition, this network activity is similar to activity that has been documented in human, non-human primate, and mammalian premotor regions tied to muscle movements. These similarities add to a growing body of literature that finds parallels between songbirds and humans in respect to the motor control of vocal organs. Given the similarities of the songbird and human motor-vocal systems these results suggest that the songbird model could be leveraged to accelerate the development of clinically translatable speech prosthesis.

neuroscience↗

bioRxiv2019

Latent space visualization, characterization, and generation of diverse vocal communication signals

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 here a set of computational methods that center around projecting animal vocalizations into low dimensional latent representational spaces that are directly learned from data. We apply these methods to diverse datasets from over 20 species, including humans, bats, songbirds, mice, cetaceans, and nonhuman primates, enabling high-powered comparative analyses of unbiased acoustic features in the communicative repertoires across species. Latent projections uncover complex features of data in visually intuitive and quantifiable ways. 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. Finally, we show how systematic sampling from latent representational spaces of vocalizations enables comprehensive investigations of perceptual and neural representations of complex and ecologically relevant acoustic feature spaces.

animal behavior and cognition↗

bioRxiv2017

A neural decoder for learned vocal behavior

Brain Machine Interfaces (BMIs) hold promise to restore impaired motor function and, because they decode neural signals to infer behavior, can serve as powerful tools to understand the neural mechanisms of motor control. Yet complex behaviors, such as vocal communication, exceed state-of-the-art decoding technologies which are currently restricted to comparatively simple motor actions. Here we present a BMI for birdsong, that decodes a complex, learned vocal behavior directly from neural activity.

neuroscience↗