Jiménez Moreno, GabrielDomínguez Morales, Juan PedroAyuso Martínez, Álvaro2025-04-292025-04-292025-01-24Ayuso Martínez, Á. (2025). Study, Design and Implementation of Neuromorphic Systems through a Spiking Boolean Computing Paradigm. (Tesis Doctoral Inédita). Universidad de Sevilla, Sevilla.https://hdl.handle.net/11441/172217In recent years, advances in transistor integration within digital computers have enabled them to be reduced to near-atomic scales, pushing this technology to its physical and thermal limits. This trend, which has also significantly increased production costs, reinforces the belief that Moore's law is going to become obsolete in the coming years. However, although doubts may arise about the possibility of further improving the efficiency of digital computers, these disappear when considering the brain, which is the most powerful and efficient system known. It is not based on transistors but on neurons and achieves high performance with minimal power consumption, both characteristics emerging mainly from the massive parallelism inherent to the nervous system. Inspired by the principles of neuromorphic engineering, this work proposes replacing transistors in digital circuits with neurons to harness these benefits. By abstracting neuronal function, it is possible to apply Boolean algebra to the design of Spiking Neural Networks under specific conditions, in a similar way to how it is applied to the design of digital circuits. Thus, this work lays the foundation for spiking Boolean computation through the spiking implementation of basic logic gates, providing a systematic approach for designing these networks, which could be valuable for researchers in the field. It also explores the development of complex spiking blocks for specialized applications, in which the development of the spiking computer is highlighted, and presents an extensive set of experiments whose results demonstrate their correct functionality mainly on two different neuromorphic platforms, SpiNNaker and Dynap-SE. The final implementations have been shown to behave as expected in challenging environments and under conditions comparable to those found in biology.application/pdf104 p.engAttribution-NonCommercial-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/openAccess