Biology subjects

Mindlin, G. B.

Publications and source records attributed to Mindlin, G. B..

4 recordsLinked to original sources

bioRxiv2026

Low-dimensional neural dynamics underlying rhythmic vocal behavior in songbirds

Birdsong is a complex learned behavior that requires millisecond-scale precision in the coordinated activation of respiratory and vocal muscles to generate sound. Canary song consists of sequences of syllables organized into phrases, in which each syllable type is repeated at a characteristic rate, giving rise to a well-defined rhythmic vocal behavior. Here, we analyze neural population activity in the telencephalic song system nucleus HVC of singing adult male canaries (Serinus canaria), in relation to both vocal output and the underlying respiratory motor gestures. To uncover structure in these high-dimensional neural recordings, we used an unsupervised autoencoder. We found that a three-dimensional latent space was sufficient to reconstruct the data with minimal information loss, revealing a low-dimensional representation of HVC population activity. The oscillation frequencies of the latent modes closely matched both the syllabic repetition rate and the corresponding respiratory motor patterns. These results show that multiunit activity in HVC captures key rhythmic features of song at the population level, providing a dynamical representation of behaviorally relevant motor structure. More broadly, our findings highlight how data-driven dimensionality reduction can reveal structured, low-dimensional neural dynamics underlying complex learned motor behaviors.

neuroscience↗

bioRxiv2023

Identification of dialects and individuals of globally threatened Yellow Cardinals using neural networks

Audio-based analysis of bird songs has proven to be a valuable practice for the growth of knowledge in the fields of ethology and ecology. In recent years, machine learning techniques applied to audio field recordings of bird calls have yielded successful results in studying population distributions and identification of individuals for their monitoring in a variety of bird species. This offers promising possibilities in the study of social behavior, biodiversity, and conservation strategies for birds. In this work, we trained deep learning models, directly from the sonograms of audio field recordings, to investigate the statistical properties of vocalizations in the endangered Yellow Cardinal. To our knowledge, this is the first time that this approach has been applied to this threatened species. Our results indicate the presence of vocal signatures that reflect similarities in songs of individuals that inhabit the same region, determining dialects, but which also show differences between individuals. These differences can be exploited by a deep learning classifier to discriminate the bird identities through their songs. Models trained with data labeled by regions showed a good performance in the recognition of dialects with a mean accuracy of (0.84 +/-0.04) significantly higher than accuracy obtained by chance. Precision and recall values also reflected the classifiers ability to find alike vocal patterns in the songs of neighboring individuals. Models trained with data labeled by individuals showed an accuracy of (0.63 +/-0.03) significantly higher than that obtained by chance. However, the individual discrimination model showed greater confusion with neighboring individuals. This reflects a hierarchical structure in the characteristics of the Yellow Cardinals vocalization, where the intra-individual variability is lower than the inter-individual variability, but it is even lower than the variability obtained when individuals inhabit different regions, providing evidence of the existence of dialects. This reinforces the results of previous works but also offers an automated method for characterizing cultural units within the species. Along with genetic data, this method could help better define management units, thereby benefiting the success of reintroduction programmes of recovered individuals of Yellow Cardinal. Moreover, the novelty of individual discrimination using neural networks for the Yellow Cardinal, which has limited data availability, shows promise for non-invasive acoustic monitoring strategies with potentially relevant implications for its conservation.

ecology↗

bioRxiv2018

Synthetic stimuli reveal a predictive and switch-like activation of the songbird’s vocal motor program

Acquisition and maintenance of complex vocal behaviors like human speech and oscine birdsong require continuous auditory feedback. The exact way in which this feedback is integrated into the vocal motor programs is not completely understood. Here we show that in sleeping zebra finches (Taeniopygia guttata), the activity of the song system selectively evoked by playbacks of their own song can be detected in the syrinx. Measuring the electrical activity of syringeal muscles, we found playback-evoked patterns identical to those recorded during song execution. Using this global and continuous readout we studied the activation dynamics of the song system elicited by different auditory stimuli. We found that a synthetic version of the birds song, rendered by a physical model of the avian phonation apparatus, evoked exactly the same response, albeit with lower efficiency. Analysis of these responses reveal a predictive and switch-like activation of the motor program, with preferred activation instants within the song.\n\nSignificanceThe study of the integration between sensory inputs and motor commands has greatly benefited from the finding that in sleeping oscine birds, playback of their own song evokes highly specific firing patterns in neurons also involved in the production of that song. Nevertheless, the sparse spiking patterns that can be recorded from few single neurons gives limited information of the overall activity of the song system. Here we show that this response is not limited to the central nervous system, but reaches vocal muscles. Combining this integrated measure of the activity of the system with surrogate synthetic songs, we found an all-or-nothing and predictive activation of the system, suggesting the existence of a pre-programmed internal dynamics.

neuroscience↗

bioRxiv2017

Discrete anatomical coordinates for speech production and synthesis

The sounds of all languages are described by a finite set of symbols, which are extracted from the continuum of sounds produced by the vocal organ. How the discrete phonemic identity is encoded in the continuous movements producing speech remains an open question for the experimental phonology. In this work, this question is assessed by using Hall-effect transducers and magnets -mounted on the tongue, lips and jaw- to track the kinematics of the oral tract during the vocalization of vowel-consonant-vowel structures. Using a threshold strategy, the time traces of the transducers were converted into discrete motor coordinates unambiguously associated with the vocalized phonemes. Furthermore, the signals of the transducers combined with the discretization strategy were used to drive a low-dimensional vocal model capable of synthesizing intelligible speech. The current work not only addressed a relevant inquiry of the biology of language, but also shows the performance of the experimental technique to monitor the displacement of the main articulators of the vocal tract while speaking. This novel electronic device represents an economic and portable option to the standard system used to study the vocal tract movements.

neuroscience↗