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Open Access Publications from the University of California

Scholarly Works (3 results)

  • Article
  • Peer Reviewed
Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 41 (2019)
We propose here a set of machine-learning algorithms to produce a generative low-dimensional and visually-understandablespace of the communicative repertoire of vocal species such as songbirds. As opposed to human speech, where individualelements are well defined and grounded in principled ways, the methods for defining units of animal communication sys-tems are often more varied and rely on human-centric heuristics. Using our method, we can automatically discover latentstructure in the vocal repertoire of individuals and use these to define-well principled categorical boundaries between vocalelements in communicating species. Further, we can sample from latent representations to generate novel vocal units thatcan be used to probe perceptual and physiological representations of communication. We demonstrate two use cases: (1)automated labeling of songbird vocal repertoires showing novel structure in vocal communication, and (2) a perceptualtask demonstrating that behavioral and physiological representational spaces can be biased by contextual information.GitHub.com/timsainb/AVGN
    Cover page: Animal Vocalization Generative Network (AVGN): A method for visualizing,understanding, and sampling from animal communicative repertoires
    • Thesis
    • Peer Reviewed
    UC San Diego Electronic Theses and Dissertations (2019)

    Parametrizing complex natural stimuli is a difficult and long-standing challenge. We used a generative deep convergent network to represent and parametrize a large corpus of song from European starlings, a songbird species, into a compressed low-dimensional space. We applied psychophysical methods to probe categorical perception of natural starling song syllables, which reveal a shared categorical perceptual space. Some categorical boundaries are sensitive to the category assignment of training syllables, indicating that the consensus is context dependent and that underlying dimensions of the space are not independent. We record simultaneous firing from populations of 10's of neurons in a secondary auditory cortical region of anesthetized starlings. By estimating how fast population level neural representation change with respect to the stimuli, we produce a measure along a path in stimuli space that is shared between birds and descriptive of the psychophysically determined parameters in other birds. Consistent with this, we predict the behavioral psychometric function along one dimension by fitting the behavior for other dimensions to the population level neural activity. Thus, knowing how the animal responds in one sub-region of the parametrized space informs responses in other sub-regions. Our results implicate the importance of experience in shaping shared perceptual boundaries among complex communication signals and suggest the categorical representation of natural signals in secondary sensory cortices is distributed much more densely than predicted by traditional hierarchical object recognition models. This thesis also explores other applications of machine learning to solve neuroscience problems, in particular, the curse of dimensionality and exploring predictive coding and surprise. A model explicitly designed to predict future states allows the compression of high-dimensional time-varying signals into a lower-dimensional representation encoding exclusively predictive and predictable information and has many practical applications.

      Cover page: The Unreasonable Effectiveness of Machine Learning in Neuroscience: Understanding High-dimensional Neural Representations with Realistic Synthetic Stimuli
      • Article
      • Peer Reviewed
      UC San Diego Previously Published Works (2019)
      Human speech possesses a rich hierarchical structure that allows for meaning to be altered by words spaced far apart in time. Conversely, the sequential structure of nonhuman communication is thought to follow non-hierarchical Markovian dynamics operating over only short distances. Here, we show that human speech and birdsong share a similar sequential structure indicative of both hierarchical and Markovian organization. We analyze the sequential dynamics of song from multiple songbird species and speech from multiple languages by modeling the information content of signals as a function of the sequential distance between vocal elements. Across short sequence-distances, an exponential decay dominates the information in speech and birdsong, consistent with underlying Markovian processes. At longer sequence-distances, the decay in information follows a power law, consistent with underlying hierarchical processes. Thus, the sequential organization of acoustic elements in two learned vocal communication signals (speech and birdsong) shows functionally equivalent dynamics, governed by similar processes.
        Cover page: Parallels in the sequential organization of birdsong and human speechCreative Commons 'BY' version 4.0 license
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