# Spike Talk in Power Electronic Grids -- Leveraging Post Moore's Computing Laws

arXiv: https://arxiv.org/abs/2411.07654 · HTML: https://mlsanity.com/paper/2411.07654/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 12 Nov 2024.

## Builds on (most cited first, 5 of 5)

- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Neuromorphic Event-Driven Semantic Communication in Microgrids](https://mlsanity.com/paper/2402.18390/) (2024)
- [Spike Talk: Genesis and Neural Coding Scheme Translations](https://mlsanity.com/paper/2408.00773/) (2024)
- [Inferring Ingrained Remote Information in AC Power Flows Using Neuromorphic Modality Regime](https://mlsanity.com/paper/2407.14883/) (2024)
- [On Noise Resiliency of Neuromorphic Inferential Communication in Microgrids](https://mlsanity.com/paper/2408.05360/) (2024)

## Abstract (arXiv)

Emerging distributed generation demands highly reliable and resilient coordinating control in microgrids. To improve on these aspects, spiking neural network is leveraged, as a grid-edge intelligence tool to establish a talkative infrastructure, Spike Talk, expediting coordination in next-generation microgrids without the need of communication at all. This paper unravels the physics behind Spike Talk from the perspective of its distributed infrastructure, which aims to address the Von Neumann Bottleneck. Relying on inferring information via power flows in tie lines, Spike Talk allows adaptive and flexible control and coordination itself, and features in synaptic plasticity facilitating online and local training functionality. Preliminary case studies are demonstrated with results, while more extensive validations are to be included as future scopes of work.