Neuromorphic Model

Neuromorphic models aim to replicate the computational efficiency and adaptability of the human brain using artificial neural networks inspired by biological neurons. Current research focuses on developing efficient neuromorphic architectures, such as spiking neural networks (SNNs) and Legendre Memory Units (LMUs), often implemented on specialized hardware to overcome limitations of traditional computing. These models are being applied to diverse tasks, including image generation, human activity recognition, and data filtering in high-energy physics, demonstrating their potential for energy-efficient computation and real-time processing in resource-constrained environments. The development of integrated toolboxes and adaptive spike sorting algorithms further enhances the accessibility and practical applicability of neuromorphic computing.