Reinforced liquid state machines—new training strategies for spiking neural networks based on reinforcements
This comparative analysis highlights the potential of feedback integration in deepened Liquid State Machines, offering insights into optimizing spiking neural networks through reinforcement-driven architectures.
Authors
- Martin Bogdan (ORCID: https://orcid.org/0000-0002-6455-127X)
- Dominik Krenzer (ORCID: https://orcid.org/0009-0001-2910-5266)
Institutions
- German Research Centre for Artificial Intelligence (DE)
- Artificial Intelligence in Medicine (Canada) (CA)
- Center for Scalable Data Analytics and Artificial Intelligence (DE)
- Leipzig University (DE)
Publication Details
- Journal
- Frontiers in Computational Neuroscience
- Published
- 2025-05-23
- DOI
- https://doi.org/10.3389/fncom.2025.1569374
- Citations
- 2
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 1.46
Funders
- Bundesministerium für Bildung und Forschung