no code implementations • 12 Oct 2021 • Ahmet Demirkaya, Tales Imbiriba, Kyle Lockwood, Sumientra Rampersad, Elie Alhajjar, Giovanna Guidoboni, Zachary Danziger, Deniz Erdogmus
Results demonstrate that state dynamics corresponding to the missing ODEs can be approximated well using a neural network trained using a recursive Bayesian filtering approach in a fashion coupled with the known state dynamic differential equations.
no code implementations • 14 Feb 2020 • Md Navid Akbar, Mathew Yarossi, Marc Martinez-Gost, Marc A. Sommer, Moritz Dannhauer, Sumientra Rampersad, Dana Brooks, Eugene Tunik, Deniz Erdoğmuş
In this work, potential DNN models are explored and the one with the minimum squared errors is recommended for the optimal performance of the M2M-Net, a network that maps transcranial magnetic stimulation of the motor cortex to corresponding muscle responses, using: a finite element simulation, an empirical neural response profile, a convolutional autoencoder, a separate deep network mapper, and recordings of multi-muscle activation.
