Enhancing Liquid State Machine Classification Through Reservoir Separability Optimization Using Swarm Intelligence and Multitask Learning

Enhancing Liquid State Machine Classification Through Reservoir Separability Optimization Using Swarm Intelligence and Multitask Learning

The Liquid State Machine (LSM) framework addresses supervised learning tasks involving spatio-temporal data streams. It relies on a randomly created, untrained Spiking Recurrent Neural Network (SRNN), called the “liquid,” to map inputs into task-independent representations. A s...

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Bibliographic Details
Main Authors: Oscar I. Alvarez-Canchila, Andres Espinal, Marco A. Sotelo-Figueroa, Jorge A. Soria-Alcaraz, Horacio Rostro-Gonzalez
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Liquid state machine
multitask learning
particle swarm optimization
reservoir computing
spiking neural networks
Online Access:https://ieeexplore.ieee.org/document/10772455/
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