activity
20172021
most citedApproximations of Shannon Mutual Information for Discrete Variables with Applications to Neural Population Coding

6 citations · 9 across the 6 of their papers we have counts for

collaborators

6 papers

cs.MA2021

Bayesian optimization of distributed neurodynamical controller models for spatial navigation

Armin Hadzic, Grace M. Hwang, Kechen Zhang +2

Dynamical systems models for controlling multi-agent swarms have demonstrated advances toward resilient, decentralized navigation algorithms. We previously introduced the NeuroSwar…

q-bio.NC2021

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

Elise Buckley, Joseph D. Monaco, Kevin M. Schultz +5

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

cs.IT20196 cited

Approximations of Shannon Mutual Information for Discrete Variables with Applications to Neural Population Coding

Wentao Huang, Kechen Zhang

Although Shannon mutual information has been widely used, its effective calculation is often difficult for many practical problems, including those in neural population coding. Asy…

q-bio.NC20171 cited

Building a Dynamical Network Model from Neural Spiking Data: Application of Poisson Likelihood

Ozgur Doruk, Kechen Zhang

Research showed that, the information transmitted in biological neurons is encoded in the instants of successive action potentials or their firing rate. In addition to that, in-viv…

q-bio.NC2017

Fitting of dynamic recurrent neural network models to sensory stimulus-response data

R. Ozgur Doruk, Kechen Zhang

We present a theoretical study aiming at model fitting for sensory neurons. Conventional neural network training approaches are not applicable to this problem due to lack of contin…

cs.IT20172 cited

Information-theoretic interpretation of tuning curves for multiple motion directions

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 f…