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Updated: May 13, 2025

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

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Published on: May 8, 2021

Boosting Reservoir Computing with Brain-inspired Adaptive Dynamics.

Keshav Srinivasan^1,2, Dietmar Plenz², Michelle Girvan^1,3,4

¹Biophysics Program, University of Maryland, College Park, MD 20740, USA.

|May 5, 2025

Summary

This summary is machine-generated.

Reservoir computers (RCs) perform best with balanced excitatory/inhibitory (E-I) signals. A novel self-adapting mechanism improves RC performance by up to 130% by adjusting E-I balance for better neural computation.

Keywords:

Brain-inspired plasticity E-I balance Heterogeneity Reservoir Computing

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Area of Science:

Computational neuroscience
Artificial intelligence
Machine learning

Background:

Reservoir computers (RCs) offer efficient computation and brain-inspired principles.
RCs simplify training by fixing internal connections but are sensitive to hyperparameters.
Standard RCs neglect crucial excitatory/inhibitory (E-I) neuronal signal balance.

Purpose of the Study:

Investigate the impact of E-I balance on RC performance.
Introduce a self-adapting mechanism for E-I balance.
Enhance RC robustness and performance across diverse tasks.

Main Methods:

Analyzed RC performance across different E-I balance regimes.
Developed a self-adapting mechanism to tune E/I balance locally.
Incorporated brain-inspired heterogeneity in target neuronal firing rates.

Main Results:

RCs perform optimally in balanced or slightly over-inhibited states.
The self-adapting mechanism improved performance by up to 130% in memory and prediction tasks.
Heterogeneity in firing rates reduced hyperparameter sensitivity and improved task versatility.

Conclusions:

Dynamic adaptation of E-I balance is superior to static optimization in RCs.
Brain-inspired mechanisms enhance RC performance, robustness, and computational understanding.
Self-adapting RCs represent a promising direction for neural computation.