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Found 4 papers, 2 papers with code
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.
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.
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.
