Neuromorphic hardware for somatosensory neuroprostheses offers a promising approach to restore natural tactile sensations by mimicking the behavior of neurons and synapses. Current neuroprosthetic devices face challenges in effectively conveying tactile feedback due to bandwidth limitations and the complexity of sensory information. Neuromorphic technology, which emulates biological neural dynamics through electronic circuits, could enhance the performance, acceptability, and embeddability of neuroprostheses. This perspective highlights the potential of integrating neuromorphic systems into neuroprostheses to create more natural human-machine interfaces. Challenges include developing efficient algorithms for decoding neural signals, designing hardware for real-time processing, and ensuring biocompatibility and stability of neural interfaces. Advances in neuromorphic computing, such as event-based sensors and spiking neuron models, are being explored to improve sensory feedback restoration. Neuromorphic hardware, with its low power consumption and parallel processing capabilities, could enable more compact and energy-efficient neuroprosthetic systems. However, translating these technologies into clinical practice requires overcoming challenges in biointegration, power management, and real-time implementation. The integration of neuromorphic technologies with advanced neural interfaces and biomimetic encoding strategies could lead to more natural and intuitive sensory feedback, improving the quality of life for individuals with sensory-motor impairments.Neuromorphic hardware for somatosensory neuroprostheses offers a promising approach to restore natural tactile sensations by mimicking the behavior of neurons and synapses. Current neuroprosthetic devices face challenges in effectively conveying tactile feedback due to bandwidth limitations and the complexity of sensory information. Neuromorphic technology, which emulates biological neural dynamics through electronic circuits, could enhance the performance, acceptability, and embeddability of neuroprostheses. This perspective highlights the potential of integrating neuromorphic systems into neuroprostheses to create more natural human-machine interfaces. Challenges include developing efficient algorithms for decoding neural signals, designing hardware for real-time processing, and ensuring biocompatibility and stability of neural interfaces. Advances in neuromorphic computing, such as event-based sensors and spiking neuron models, are being explored to improve sensory feedback restoration. Neuromorphic hardware, with its low power consumption and parallel processing capabilities, could enable more compact and energy-efficient neuroprosthetic systems. However, translating these technologies into clinical practice requires overcoming challenges in biointegration, power management, and real-time implementation. The integration of neuromorphic technologies with advanced neural interfaces and biomimetic encoding strategies could lead to more natural and intuitive sensory feedback, improving the quality of life for individuals with sensory-motor impairments.