Search Results for author: Kechen Zhang

Found 6 papers, 0 papers with code

Bayesian optimization of distributed neurodynamical controller models for spatial navigation

no code implementations31 Oct 2021 Armin Hadzic, Grace M. Hwang, Kechen Zhang, Kevin M. Schultz, Joseph D. Monaco

Dynamical systems models for controlling multi-agent swarms have demonstrated advances toward resilient, decentralized navigation algorithms.

Efficient Exploration Gaussian Processes

An interdisciplinary approach to high school curriculum development: Swarming Powered by Neuroscience

no code implementations12 Sep 2021 Elise Buckley, Joseph D. Monaco, Kevin M. Schultz, Robert Chalmers, Armin Hadzic, Kechen Zhang, Grace M. Hwang, M. Dwight Carr

This article discusses how to create an interactive virtual training program at the intersection of neuroscience, robotics, and computer science for high school students.

Cognitive swarming in complex environments with attractor dynamics and oscillatory computing

no code implementations15 Sep 2019 Joseph D. Monaco, Grace M. Hwang, Kevin M. Schultz, Kechen Zhang

Animals and many-robot groups must solve common problems of navigating complex and uncertain environments.

Information-theoretic interpretation of tuning curves for multiple motion directions

no code implementations1 Feb 2017 Wentao Huang, Xin Huang, Kechen Zhang

We have developed an efficient information-maximization method for computing the optimal shapes of tuning curves of sensory neurons by optimizing the parameters of the underlying feedforward network model.

An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax

no code implementations7 Nov 2016 Wentao Huang, Kechen Zhang

Starting from the initial solution, an efficient algorithm based on gradient descent of the final objective function is proposed to learn representations from the input datasets, and the method works for complete, overcomplete, and undercomplete bases.

Representation Learning

Information-Theoretic Bounds and Approximations in Neural Population Coding

no code implementations4 Nov 2016 Wentao Huang, Kechen Zhang

While Shannon's mutual information has widespread applications in many disciplines, for practical applications it is often difficult to calculate its value accurately for high-dimensional variables because of the curse of dimensionality.

Dimensionality Reduction

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