The field of neuromorphic computing and wireless networks is rapidly evolving, with a focus on developing innovative solutions for next-generation computing platforms. Researchers are exploring new design directions for MAC protocols that are tightly integrated with the underlying channel physics, enabling low-latency and high-throughput communication. Additionally, there is a growing interest in using nonlinear threshold logic gates, such as the receptron, to enhance classification capabilities for analog inputs. Neuromorphic computing is also being applied to edge applications, including wireless sensing and audio recognition, using resonate-and-fire neurons to process time-domain signals directly. Furthermore, spiking neural networks are being investigated for energy-efficient processing of synthetic aperture radar interferometric phase unwrapping, offering potential energy savings of 30-100x compared to conventional approaches. Notable papers in this area include:

• A paper presenting TRMAC, a novel cross-layer MAC protocol that exploits the spatial focusing capability of Time Reversal to enable multiple parallel transmissions over a shared frequency channel.
• A study on the receptron, a nonlinear threshold logic gate with intrinsic multi-dimensional selective capabilities for analog inputs, demonstrating its potential for edge applications requiring high selectivity and classification capabilities.
• Research on a wireless split computing architecture employing resonate-and-fire neurons for energy-efficient processing of time-series signals.
• A theoretical framework for applying spiking neural networks to synthetic aperture radar interferometric phase unwrapping, offering a complementary approach to existing algorithms for sustainable large-scale InSAR processing.