Skip to main content
Search

# Reservoir Computing

2 related articles

Subscribe to updates
  1. bioRxiv

    Closed-loop brain stimulation stops chasing a fixed waveform: CLEAR-FORCE folds brain feedback into the error, inducing and suppressing oscillations in simulation

    Closed-loop brain-stimulation controllers are usually trained against a preset target and struggle to follow real-time fluctuations in brain activity. CLEAR-FORCE combines the reservoir network's output error with a feedback error taken from the population membrane potential of a simulated spiking network, updating readout weights online while stimulation runs. In two spiking-neuron simulations it induced 30 Hz oscillations and suppressed burst events; error was lower than open loop when feedback strength was above zero, but weaker feedback did worse than open loop.

    University of Ottawa
    Athena Moreno-Gris（First author）Jean‐Philippe Thivierge（Corresponding author）
    CLEAR-FORCEReservoir ComputingClosed-loop brain stimulationBurst suppression
  2. arXiv

    Recurrent memory no longer needs training: frozen networks recall over 256 steps with half the trainable parameters

    In partially observable reinforcement learning, freezing the recurrent memory module and training only the output and mixer layers still matches fully trained counterparts on delayed recall, discrete and continuous control, and pixel-based tasks, while cutting trainable parameters by more than half.

    University of Macau
    Zekang Wang（First author）
    Frozen Recurrent NetworkReservoir ComputingPartially Observable Markov Decision ProcessReinforcement LearningGated Recurrent Unit

You’re all caught up