Boosting Reservoir Computing with Brain-inspired Adaptive Dynamics

2025-04-16

AI Summary

Reservoir Computing (RC) suffers from sensitivity to hyperparameters and limited neurobiological plausibility. To address these limitations, this work proposes a brain-inspired, locally adaptive excitatory-inhibitory (E/I) dynamic regulation mechanism. The method employs heterogeneous target firing rates and online local feedback control to enable autonomous homeostatic regulation of population firing rates—eliminating the need for fine-tuning global hyperparameters. Its core innovation lies in the first integration of a neurodynamically grounded, local E/I balance mechanism into the RC architecture, thereby enhancing intrinsic temporal modeling capability and memory capacity. Experiments demonstrate up to 130% performance improvement on standard memory capacity benchmarks and multi-class time-series prediction tasks, alongside significantly improved robustness. Moreover, the approach exhibits superior generalization across diverse linear and nonlinear tasks.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Bio-inspired Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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 for precise hyperparameter tuning, we introduce a self-adapting mechanism that locally adjusts E/I balance to achieve target neuronal firing rates, improving performance by up to 130% in tasks like memory capacity and time series prediction compared with globally tuned RCs. Incorporating brain-inspired heterogeneity in target neuronal firing rates further reduces the need for fine-tuning hyperparameters and enables RCs to excel across linear and non-linear tasks. These results support a shift from static optimization to dynamic adaptation in reservoir design, demonstrating how brain-inspired mechanisms improve RC performance and robustness while deepening our understanding of neural computation.

Problem

• Enhancing reservoir computers with brain-inspired E-I balance
• Reducing hyperparameter sensitivity via adaptive neuronal dynamics
• Improving performance in memory and prediction tasks

Innovation

• Self-adapting E-I balance for firing rates
• Brain-inspired heterogeneous target firing rates
• Dynamic adaptation replacing static optimization