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Similar Auditory Cortical Suppression by Distinct Mechanisms: Homeostasis, Inhibition, and Background Noise

  • Author(s): Seybold, Bryan Andrew
  • Advisor(s): Schreiner, Christoph E
  • et al.
Abstract

The auditory cortex is critical for the understanding of speech. This task is accomplished through the nonlinear network interactions between many neurons. Cortical neurons are grouped into distinct types depending on whether they release excitatory or inhibitory neurotransmitters and molecular, biophysical, and morphological properties. Because cortical network interactions are nonlinear, perturbing these networks can produce counterintuitive results. To understand how auditory cortex accomplishes complex tasks like speech comprehension, we need to understand how nonlinearities shape network processing. This dissertation provides examples in rodent auditory cortex of manipulations that produce straightforward effects in single cells or small portions of the parameter range, but, in some cases, opposite effects in cortical networks. In chapter 1, I chronically reduced the level of inhibition in the cortex using Dlx1 knockout mice, which should expand frequency tuning in auditory cortex, but observed reduced frequency tuning. Homeostatic changes over time nonlinearly changed expansion into reduction. In chapter 2, I acutely activated two populations of interneurons that express either somatostatin or parvalbumin, which produce different forms of linear suppression in vitro, but observed the same suppression in vivo. The nonlinear elements of the recurrent cortical network obscured the type of linear suppression. In chapter 3, I added background noise, which suppress tone-evoked firing rates quasi-linearly at low intensities, but observed that noise-related suppression increased nonlinearly with noise intensity. The nonlinear mechanisms that preserve stimulus information in the presence of noise are less robust at high noise intensities. In each chapter, nonlinear effects led to unexpected results, highlighting the need to interpret results in the context of nonlinear networks to understand cortical processing.

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