# On Noise Resiliency of Neuromorphic Inferential Communication in Microgrids

arXiv: https://arxiv.org/abs/2408.05360 · HTML: https://mlsanity.com/paper/2408.05360/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2024.

## Built on by (most cited first, 1 of 1)

- [Spike Talk in Power Electronic Grids -- Leveraging Post Moore's Computing Laws](https://mlsanity.com/paper/2411.07654/) (2024)

## Abstract (arXiv)

Neuromorphic computing leveraging spiking neural network has emerged as a promising solution to tackle the security and reliability challenges with the conventional cyber-physical infrastructure of microgrids. Its event-driven paradigm facilitates promising prospect in resilient and energy-efficient coordination among power electronic converters. However, different from biological neurons that are focused in the literature, microgrids exhibit distinct architectures and features, implying potentially diverse adaptability in its capabilities to dismiss information transfer, which remains largely unrevealed. One of the biggest drawbacks in the information transfer theory is the impact of noise in the signaling accuracy. Hence, this article hereby explores the noise resiliency of neuromorphic inferential communication in microgrids through case studies and underlines potential challenges and solutions as extensions beyond the results, thus offering insights for its implementation in real-world scenarios.