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Open Access Publications from the University of California

Scholarly Works (4 results)

  • Thesis
  • Peer Reviewed
UC San Francisco Electronic Theses and Dissertations (2017)

The ability of the human brain to represent senses reliably and command a motor response is central to our ability to respond to the world around us. Here, I aim to understand these processes through simulation and experimental analysis. First I developed a recurrent neural network as a model of the brain receiving approximate sensory input. By learning to capture the distribution of the representation of a sense, the network learns the dynamics of the underlying stimulus and learns to integrate information near optimally over time, using recent estimates of position and velocity to inform the current estimate of the state of the object.

Next, I analyzed the cortical representation of auditory speech. Syllables were played to human subjects while recording voltage fluctuations directly from their brains using electrocorticography. I found that the variability of the neural activity in the superior temporal gyrus, an auditory cortical region, was “quenched” upon stimulus presentation. Furthermore, this decrease in variability is coincident with stimulus representation, and enables the brain to represent a stimulus more accurately.

Then, I examined the cortical control of laryngeal functions in humans using electrocorticography during produced speech. I found that the dorsal laryngeal motor cortex controls modulations of vocal pitch. Activity in that region is correlated with pitch in speech and in song. The representation of pitch in this region is separable from voicing, showing multiple dimensions of control represented in the cortex. Through cortical stimulation, I show that activity in this area caused proportional laryngeal muscle activation. I discuss how these findings may add important information furthering our understanding the evolution of speech in humans. Finally, I discuss how these signals can be used to decode prosodic patterns directly from neural activity for use in a speech prosthetic.

    Cover page: Neural signals and control of the larynx
    • Article
    • Peer Reviewed
    UC San Francisco Previously Published Works (2016)

    Unlabelled

    Accurate sensory discrimination is commonly believed to require precise representations in the nervous system; however, neural stimulus responses can be highly variable, even to identical stimuli. Recent studies suggest that cortical response variability decreases during stimulus processing, but the implications of such effects on stimulus discrimination are unclear. To address this, we examined electrocorticographic cortical field potential recordings from the human nonprimary auditory cortex (superior temporal gyrus) while subjects listened to speech syllables. Compared with a prestimulus baseline, activation variability decreased upon stimulus onset, similar to findings from microelectrode recordings in animal studies. We found that this decrease was simultaneous with encoding and spatially specific for those electrodes that most strongly discriminated speech sounds. We also found that variability was predominantly reduced in a correlated subspace across electrodes. We then compared signal and variability (noise) correlations and found that noise correlations reduce more for electrodes with strong signal correlations. Furthermore, we found that this decrease in variability is strongest in the high gamma band, which correlates with firing rate response. Together, these findings indicate that the structure of single-trial response variability is shaped to enhance discriminability despite non-stimulus-related noise.

    Significance statement

    Cortical responses can be highly variable to auditory speech sounds. Despite this, sensory perception can be remarkably stable. Here, we recorded from the human superior temporal gyrus, a high-order auditory cortex, and studied the changes in the cortical representation of speech stimuli across multiple repetitions. We found that neural variability is reduced upon stimulus onset across electrodes that encode speech sounds.
      Cover page: Dynamic Structure of Neural Variability in the Cortical Representation of Speech Sounds
      • Article
      • Peer Reviewed
      UC San Francisco Previously Published Works (2018)
      In speech, the highly flexible modulation of vocal pitch creates intonation patterns that speakers use to convey linguistic meaning. This human ability is unique among primates. Here, we used high-density cortical recordings directly from the human brain to determine the encoding of vocal pitch during natural speech. We found neural populations in bilateral dorsal laryngeal motor cortex (dLMC) that selectively encoded produced pitch but not non-laryngeal articulatory movements. This neural population controlled short pitch accents to express prosodic emphasis on a word in a sentence. Other larynx cortical representations controlling voicing and longer pitch phrase contours were found at separate sites. dLMC sites also encoded vocal pitch during a non-speech singing task. Finally, direct focal stimulation of dLMC evoked laryngeal movements and involuntary vocalization, confirming its causal role in feedforward control. Together, these results reveal the neural basis for the voluntary control of vocal pitch in human speech. VIDEO ABSTRACT.
        Cover page: The Control of Vocal Pitch in Human Laryngeal Motor Cortex
        • Article
        • Peer Reviewed
        UC San Francisco Previously Published Works (2022)
        The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our open-source software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWB's impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem, which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness.
          Cover page: The Neurodata Without Borders ecosystem for neurophysiological data science.
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