Nicola R Heneghan, Madeleine Nazareth, Wendy J Johnson, Isaak Tyros, Jackie Sadi, Heather Gillis, Alison B Rushton
Experiences of telehealth e-mentoring within postgraduate musculoskeletal physical therapy education in the UK and Canada: a protocol for parallel mixed-methods studies and cross-cultural comparison
BMJ open
insect flight strongly nonlinear actuated dynamical system strategies understanding control typically relied either modelbased methods linearizations thereof develop framework combines model predictive control established flight dynamics model deep neural networks dnn create efficient method solving inverse problem flight control turn natural systems inspiration since inherently demonstrate network pruning consequence yielding efficient networks specific set tasks bioinspired approach allows us leverage network pruning optimally sparsify dnn architecture order perform flight tasks neural connections possible however limits sparsification specifically number connections falls critical threshold flight performance drops considerably develop sparsification paradigms explore limits control tasks monte carlo simulations also quantify statistical distribution network weights pruning given initial random weights dnns demonstrate average network pruned retain small amount original network weights still perform comparably fullyconnected counterpart relative number remaining weights however highly dependent initial architecture size network overall work shows sparsely connected dnns capable predicting forces required follow flight trajectories additionally sparsification sharp performance limits