Neurally driven synthesis of learned, complex vocalizations

Arneodo, Ezequiel M.; Chen, Shukai; Brown, Daril E.; Gilja, Vikash; Gentner, Timothy Q.

doi:10.1016/j.cub.2021.05.035

Cited by 6 publications

(8 citation statements)

References 68 publications

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“…Audio (vocalizations) was recorded through a microphone (Earthworks M30) connected to a preamplifier (ART Tube MP) and registered temporally to ongoing neural activity ( Fig 1 )]. Extracellular voltage waveforms and pre-amplified audio were amplified and digitized at 30 kHz using an intan RHD2000 acquisition system, Open Ephys and custom software ( Fig 1D ) [ 72 ].…”

Section: Methodsmentioning

confidence: 99%

“…The mass of the probe, Microdrive, and protective chamber were measured to be 1.2–1.4g. Upon the finches returning to a single-housing cage, a weight reliever mechanism was attached using the end of a thin nylon wire that was attached to an ad-hoc pin in the chamber; the other end routed through a set of pulleys and attached to a counterweight mass of ~1g [ 72 ].…”

Section: Methodsmentioning

confidence: 99%

“…A custom template matching algorithm written in Python was used to find potential instances of vocal activity (using an exemplar motif as the template); these instances were then curated manually to rule out false positives [ 72 ]. The curated motif start times were grouped into larger time segments that ensured that the gap between each motif was no greater than 20 seconds ( Fig 2B ).…”

Section: Methodsmentioning

confidence: 99%

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Local field potentials in a pre-motor region predict learned vocal sequences

et al. 2021

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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 detail patterns of sequential bursting in small, carefully identified subsets of neurons in the HVC population. The dynamics of HVC are well described by these characterizations, but have not been verified beyond this scale of measurement. 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 they have been used to study and decode human speech and other complex motor behaviors. Here we characterize LFP signals presumptively from the 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 the utility of LFP for studying vocal behavior in songbirds. Surprisingly, the time frequency structure of 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 into common computational principles for learning and/or generating complex motor-vocal behaviors.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Local field potentials in a pre-motor region predict learned vocal sequences

et al. 2021

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“…Related source-filter models have been developed to synthesize birdsong based upon underlying physiological mechanisms (Fee et al, 1998 ; Sitt et al, 2008 , 2010 ; Arneodo and Mindlin, 2009 ; Arneodo et al, 2012 ). Recently, Arneodo et al ( 2021 ) demonstrated that synthetic source-filter models can be coupled with neural recordings accurately reconstruct vocalizations from neural data alone. One drawback of source-filter models is the difficulty with which they can be fitted to the diversity of non-human vocalizations that exist.…”

Section: Synthesizing Vocalizationsmentioning

confidence: 99%

“…A clear advantage to the approaches discussed in Section 5 is that we can learn to bring complex vocal behavioral spaces into a compressive low-dimensional behavior spaces, even without a prior model of the structure in that space. For example, Arneodo et al ( 2021 ) find that directly predicting the acoustic structure of zebra finch song from neural data does not perform as well as predicting the parameters of a low-dimensional biophysical model of song production. In the many species in which we do not have access to a biophysical model of vocal production, learned acoustic spaces may be a viable alternative.…”

Section: Mapping Vocal Communication To Perception Behavior and Physiologymentioning

confidence: 99%

Toward a Computational Neuroethology of Vocal Communication: From Bioacoustics to Neurophysiology, Emerging Tools and Future Directions

Sainburg

Gentner

2021

Front. Behav. Neurosci.

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Recently developed methods in computational neuroethology have enabled increasingly detailed and comprehensive quantification of animal movements and behavioral kinematics. Vocal communication behavior is well poised for application of similar large-scale quantification methods in the service of physiological and ethological studies. This review describes emerging techniques that can be applied to acoustic and vocal communication signals with the goal of enabling study beyond a small number of model species. We review a range of modern computational methods for bioacoustics, signal processing, and brain-behavior mapping. Along with a discussion of recent advances and techniques, we include challenges and broader goals in establishing a framework for the computational neuroethology of vocal communication.

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Neuroendocrine Modulation of Coordinated Acoustic Signals

Tobiansky,

Price

2024

Masterclass in Neuroendocrinology

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Neurally driven synthesis of learned, complex vocalizations

Cited by 6 publications

References 68 publications

Local field potentials in a pre-motor region predict learned vocal sequences

Local field potentials in a pre-motor region predict learned vocal sequences

Toward a Computational Neuroethology of Vocal Communication: From Bioacoustics to Neurophysiology, Emerging Tools and Future Directions

Neuroendocrine Modulation of Coordinated Acoustic Signals

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