LsmGrid: A Data-Aware Neuroevolution Framework for Designing Minimal Liquid State Machines

Abstract

Liquid State Machines (LSM) are spiking recurrent neural networks inspired by the dynamics of the human brain. By encoding information as discrete spikes, they enable low-power and real-time data processing at the edge. Despite the benefits of this computational model, the design of efficient LSM still lacks a widely accepted methodological foundation. Existing approaches often rely on arbitrary parameter tuning and large reservoir sizes, which are computationally costly and demand prior domain-specific knowledge. In this work, we introduce a systematic methodology for simplifying LSM design while substantially reducing the number of required neurons and synapses. This significantly reduces power consumption, bringing this class of models closer to their original motivation of ultra-low-power processing directly at the sensor level. A hybrid metaheuristic combining hill climbing and genetic programming (HC-GP) optimizes the LSM design, guiding the network to efficiently capture input patterns while avoiding random connectivity inefficiencies. Experimental results on the N-TIDIGITS and FSDD benchmarks demonstrate the effectiveness of the approach, achieving 82.3% accuracy on N-TIDIGITS, significantly surpassing existing models of comparable size, and 92.7% on FSDD, achieving state-of-the-art accuracy with only 66 neurons (6.6% of the neurons reported in previous state-of-the-art networks).