Description: Hybrid systems are architectures that combine different computing paradigms to leverage the advantages of each. These systems aim to integrate the efficiency and quick response of real-time systems with the learning and adaptation capabilities of neuromorphic models. This combination enables the development of applications that require immediate processing while also having the ability to learn from data in real time. Hybrid systems are particularly relevant in environments where rapid and accurate decision-making is crucial, such as in robotics, industrial automation, and autonomous vehicles. The flexibility of these systems allows them to adapt to various conditions and requirements, optimizing performance and energy efficiency. In summary, hybrid systems represent an evolution in how computing technologies are designed and utilized, integrating the best of different approaches to tackle complex problems more effectively.
