Work

The Reinforced Liquid State Machine: A New Training Architecture for Spiking Neural Networks

Year: 2025

Type: conference-paper

Abstract: This work presents a novel Spiking Neural Network training architecture based on a deepened Liquid State Machine integrating Winner-Takes-All computation and Reward-Modulated Synaptic Plasticity.The networks performance is evaluated on the Heidelberg Dataset for spoken digit recognition.A two-layer liquid configuration improves classification accuracy by 5% over a single-layer baseline, while incorporating feedback between liquid layers.This architecture demonstrates that deep liquid models, combined with feedback and reward-driven learning, can effectively capture complex spatio-temporal patterns, offering significant advantages in terms of accuracy over traditional Liquid State Machines.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-1

Authors: Dominik Krenzer, Martin Bogdan

Institution: Leipzig University

Language: English

Topic: Advanced Memory and Neural Computing

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

Funder: Bundesministerium für Bildung und Forschung

Award: G3740268834