Boosting Reservoir Computing with Brain-inspired Adaptive Dynamics.

ABSTRACT: Reservoir computers (RCs) provide a computationally efficient alternative to deep learning while also offering a framework for incorporating brain-inspired computational principles. By using an internal neural network with random, fixed connections-the 'reservoir'-and training only the output weights, RCs simplify the training process but remain sensitive to the choice of hyperparameters that govern activation functions and network architecture. Moreover, typical RC implementations overlook a critical aspect of neuronal dynamics: the balance between excitatory and inhibitory (E-I) signals, which is essential for robust brain function. We show that RCs characteristically perform best in balanced or slightly over-inhibited regimes, outperforming excitation-dominated ones. To reduce the need

SUBMITTER: Srinivasan K 

PROVIDER: S-EPMC12047930 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature