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Open Access Publications from the University of California

Scholarly Works (2 results)

  • Thesis
  • Peer Reviewed
UC Berkeley Electronic Theses and Dissertations (2011)

Despite remarkable progress in understanding the neurophysiology of cortex, fundamental questions regarding its function remain unanswered. The present study explores how a local population of neurons along a cortical column in visual cortex responds to dynamic natural stimuli. I show how a novel application of sparse coding to neural recordings obtained with high-density laminar probes can separate the complex statistical structure of this data into biophysically interpretable, underlying causes. The resulting data representation is instrumental in characterizing both spiking and local field potential activity across lamina in response to natural movies. Finally, I present a framework for understanding laminar population activity in terms of a statistical model that accounts for network interactions as well as the driving influence of the stimulus.

    Cover page: The laminar organization of V1 neural activity in response to dynamic natural scenes
    • Article
    • Peer Reviewed
    UC Berkeley Previously Published Works (2023)
    A prominent approach to solving combinatorial optimization problems on parallel hardware is Ising machines, i.e., hardware implementations of networks of interacting binary spin variables. Most Ising machines leverage second-order interactions although important classes of optimization problems, such as satisfiability problems, map more seamlessly to Ising networks with higher-order interactions. Here, we demonstrate that higher-order Ising machines can solve satisfiability problems more resource-efficiently in terms of the number of spin variables and their connections when compared to traditional second-order Ising machines. Further, our results show on a benchmark dataset of Boolean k-satisfiability problems that higher-order Ising machines implemented with coupled oscillators rapidly find solutions that are better than second-order Ising machines, thus, improving the current state-of-the-art for Ising machines.
      Cover page: Efficient optimization with higher-order ising machines.
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