Visible to the public Biblio

Found 1750 results

Journal Article
Ivanov, Radoslav, Pajic, Miroslav, Lee, Insup.  2016.  Attack-Resilient Sensor Fusion for Safety-Critical Cyber-Physical Systems. ACM Transactions on Embedded Computing Systems. 15:21:1–21:24.
M. Pajic, I. Lee, G. J. Pappas.  2017.  Attack-Resilient State Estimation for Noisy Dynamical Systems. IEEE Transactions on Control of Network Systems. 4:82-92.
Brugarolas, R., Valero-Sarmiento, J. M., Bozkurt, A., Essick, G..  2016.  Auto-Adjusting Mandibular Repositioning Device for In-Home Use. 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Orlando, FL
G. Gay, M. Staats, M. Whalen, M. P. E. Heimdahl.  2015.  Automated Oracle Data Selection Support. IEEE Transactions on Software Engineering. 41:1119-1137.
Pierre{-}Marc Jodoin, Venkatesh Saligrama, Janusz Konrad.  2012.  Behavior Subtraction. {IEEE} Trans. Image Processing. 21:4244–4255.
Abbas, Houssam, Jang, Kuk Jin, Mangharam, Rahul.  2016.  Benchmark: Nonlinear Hybrid Automata Model of Excitable Cardiac Tissue. Applied Verification for Continuous and Hybrid Systems.
W. S. Grant, J. Tanner, L. Itti.  2017.  Biologically plausible learning in neural networks with modulatory feedback. Neural Networks. 88:32-48.

Although Hebbian learning has long been a key component in understanding neural plasticity, it has not yet been successful in modeling modulatory feedback connections, which make up a significant portion of connections in the brain. We develop a new learning rule designed around the complications of learning modulatory feedback and composed of three simple concepts grounded in physiologically plausible evidence. Using border ownership as a prototypical example, we show that a Hebbian learning rule fails to properly learn modulatory connections, while our proposed rule correctly learns a stimulus-driven model. To the authors' knowledge, this is the first time a border ownership network has been learned. Additionally, we show that the rule can be used as a drop-in replacement for a Hebbian learning rule to learn a biologically consistent model of orientation selectivity, a network which lacks any modulatory connections. Our results predict that the mechanisms we use are integral for learning modulatory connections in the brain and furthermore that modulatory connections have a strong dependence on inhibition.

Shang-Li Wu, Homayoon Kazerooni.  2017.  Biomechanical Design of a Mechanical Exoskeleton Knee. IEEE/RSJ International Conference on Intelligent Robots and Systems.