The field of neuromorphic computing and robotics is experiencing significant growth, driven by innovations in brain-inspired computing, sensorimotor integration, and soft robotics. Researchers are exploring new ways to mimic the human brain's neural architecture and sensory systems to create more adaptive, efficient, and interactive machines. A key direction in this field is the development of novel neuromorphic models and algorithms that can effectively process and interpret complex sensory information, such as heat, touch, and proprioception. These advancements have the potential to revolutionize applications in areas like prosthetics, autonomous vehicles, and IoT networks. Noteworthy papers in this area include those that propose groundbreaking threat models for neuromorphic computing, bio-mimetic models for heat-evoked nociceptive withdrawal reflex, and innovative platforms for object classification using neuromorphic proprioceptive signals. For example, one paper introduces a pioneering exploration of neuromorphic mimicry attacks, while another presents a bio-plausible neuromorphic model for heat-evoked nociceptive withdrawal reflex. Additionally, a novel platform integrates a soft anthropomorphic robot hand with flexible proprioceptive sensors and a classifier that utilizes a hybrid spiking neural network.