Search Results for author:
Found 8 papers, 3 papers with code
Forward propagation closed loop learning
For an autonomous agent, the inputs are the sensory data that inform the agent of the state of the world, and the outputs are their actions, which act on the world and consequently produce new sensory inputs.
Real-time noise cancellation with Deep Learning
Biological measurements are often contaminated with large amounts of non-stationary noise which require effective noise reduction techniques.
Closed-loop deep learning: generating forward models with back-propagation
Here, we show how this can be directly achieved by embedding deep learning into a closed loop system and preserving its continuous processing.
Sign and Relevance Learning
Standard models of biologically realistic or biologically inspired reinforcement learning employ a global error signal, which implies the use of shallow networks.
BCI-Walls: A robust methodology to predict success or failure in brain computer interfaces
The results show that facial muscle activity and eye-movements have a strong impact on the detectability of EEG and that minimising both eye-movement artefacts and muscle noise is essential to be able to detect conscious EEG changes.
Prime and Modulate Learning: Generation of forward models with signed back-propagation and environmental cues
In this work we follow a different approach which is particularly applicable to closed-loop learning of forward models where back-propagation makes exclusive use of the sign of the error signal to prime the learning, whilst a global relevance signal modulates the rate of learning.
Model Checking for Closed-Loop Robot Reactive Planning
Our approach is based on chaining temporary control systems which are spawned to eliminate disturbances in the local environment that disrupt an autonomous agent from its preferred action (or resting state).
Homeostatic motion planning with innate physics knowledge
Living organisms interact with their surroundings in a closed-loop fashion, where sensory inputs dictate the initiation and termination of behaviours.
