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Creators/Authors contains: "Park, I. M."

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  1. ; ; ; (, Asilomar Conference on Signals, Systems and Computers)
    Exploding and vanishing gradient are both major problems often faced when an artificial neural network is trained with gradient descent. Inspired by the ubiquity and robustness of nonlinear oscillations in biological neural systems, we investigate the properties of their artificial counterpart, the stable limit cycle neural networks. Using a continuous time dynamical system interpretation of neural networks and backpropagation, we show that stable limit cycle neural networks have non-exploding gradients, and at least one effective non-vanishing gradient dimension. We conjecture that limit cycles can support the learning of long temporal dependence in both biological and artificial neural networks. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; ; (, Conference record - Asilomar Conference on Signals, Systems, & Computers)
    To understand the complex nonlinear dynamics of neural circuits, we fit a structured state-space model called tree-structured recurrent switching linear dynamical system (TrSLDS) to noisy high-dimensional neural time series. TrSLDS is a multi-scale hierarchical generative model for the state-space dynamics where each node of the latent tree captures locally linear dynamics. TrSLDS can be learned efficiently and in a fully Bayesian manner using Gibbs sampling. We showcase TrSLDS' potential of inferring low-dimensional interpretable dynamical systems on a variety of examples. 
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    Accepted Manuscript Full Text Available