Boosting reservoir computing with brain-inspired adaptive control of E-I balance.

ABSTRACT: Reservoir computers (RCs) are a class of recurrent neural networks that incorporate brain-inspired principles and provide an efficient alternative to deep learning. With fixed random internal connections and trained output weights, they simplify learning but remain sensitive to hyperparameters governing activation and connectivity. While various hyperparameters are commonly tuned, the relative balance between excitatory and inhibitory (E-I) signals-fundamental to brain function-is typically fixed in RCs. Here, we investigate tuning this balance and show that strong performance consistently arises in balanced or slightly over-inhibited regimes, not excitation-dominated ones. Further, we introduce a self-adapting mechanism that locally adjusts E-I balance to achieve target firing rates, redu

SUBMITTER: Srinivasan K 

PROVIDER: S-EPMC12635267 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature