AIMC Topic: Feedback

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A Modular 3-Degrees-of-Freedom Force Sensor for Robot-Assisted Minimally Invasive Surgery Research.

Sensors (Basel, Switzerland)
Effective force modulation during tissue manipulation is important for ensuring safe, robot-assisted, minimally invasive surgery (RMIS). Strict requirements for in vivo applications have led to prior sensor designs that trade off ease of manufacture ...

A force-sensing surgical drill for real-time force feedback in robotic mastoidectomy.

International journal of computer assisted radiology and surgery
PURPOSE: Robotic assistance in otologic surgery can reduce the task load of operating surgeons during the removal of bone around the critical structures in the lateral skull base. However, safe deployment into the anatomical passageways necessitates ...

FPGA-Based In-Vivo Calcium Image Decoding for Closed-Loop Feedback Applications.

IEEE transactions on biomedical circuits and systems
Miniaturized calcium imaging is an emerging neural recording technique that has been widely used for monitoring neural activity on a large scale at a specific brain region of rats or mice. Most existing calcium-image analysis pipelines operate offlin...

Atomically thin optomemristive feedback neurons.

Nature nanotechnology
Cognitive functions such as learning in mammalian brains have been attributed to the presence of neuronal circuits with feed-forward and feedback topologies. Such networks have interactions within and between neurons that provide excitory and inhibit...

Aperiodic switching event-triggered stabilization of continuous memristive neural networks with interval delays.

Neural networks : the official journal of the International Neural Network Society
The stabilization problem is studied for memristive neural networks with interval delays under aperiodic switching event-triggered control. Note that, most of delayed memristive neural networks models studied are discontinuous, which are not the real...

Event-triggered control for robust exponential synchronization of inertial memristive neural networks under parameter disturbance.

Neural networks : the official journal of the International Neural Network Society
Synchronization of memristive neural networks (MNNs) by using network control scheme has been widely and deeply studied. However, these researches are usually restricted to traditional continuous-time control methods for synchronization of the first-...

Tree-structured neural networks: Spatiotemporal dynamics and optimal control.

Neural networks : the official journal of the International Neural Network Society
How the network topology drives the response dynamic is a basic question that has not yet been fully answered in neural networks. Elucidating the internal relation between topological structures and dynamics is instrumental in our understanding of br...

Optimal H tracking control of nonlinear systems with zero-equilibrium-free via novel adaptive critic designs.

Neural networks : the official journal of the International Neural Network Society
In this paper, a novel adaptive critic control method is designed to solve an optimal H tracking control problem for continuous nonlinear systems with nonzero equilibrium based on adaptive dynamic programming (ADP). To guarantee the finiteness of a c...

Dynamic event-triggered controller design for nonlinear systems: Reinforcement learning strategy.

Neural networks : the official journal of the International Neural Network Society
The current investigation aims at the optimal control problem for discrete-time nonstrict-feedback nonlinear systems by invoking the reinforcement learning-based backstepping technique and neural networks. The dynamic-event-triggered control strategy...

Meta-learning biologically plausible plasticity rules with random feedback pathways.

Nature communications
Backpropagation is widely used to train artificial neural networks, but its relationship to synaptic plasticity in the brain is unknown. Some biological models of backpropagation rely on feedback projections that are symmetric with feedforward connec...