A Neural Dynamic Architecture for Reaching and Grasping Integrates Perception and Movement Generation and Enables On-Line Updating

Towards a synergy framework across neuroscience and robotics: Lessons learned and open questions. Reply to comments on: “Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands”

(2016) Marco Santello et al.   Physics of Life Reviews

Dexterous grasping under shape uncertainty

(2016) Miao Li et al.   ROBOTICS AND AUTONOMOUS SYSTEMS

Modeling human target reaching with an adaptive observer implemented with dynamic neural fields

(2015) Farzaneh S. Fard et al.   NEURAL NETWORKS

Choice reaching with a LEGO arm robot (CoRLEGO): The motor system guides visual attention to movement-relevant information

(2015) Soeren Strauss et al.   NEURAL NETWORKS

The Challenge of Understanding the Brain: Where We Stand in 2015

(2015) John Lisman   NEURON

Learning of grasp selection based on shape-templates

(2013) Alexander Herzog et al.   AUTONOMOUS ROBOTS

Dynamic Neural Fields as Building Blocks of a Cortex-Inspired Architecture for Robotic Scene Representation

(2011) Stephan K. U. Zibner et al.   IEEE Transactions on Autonomous Mental Development

Intention, Action Planning, and Decision Making in Parietal-Frontal Circuits

(2009) Richard A. Andersen et al.   NEURON