# An Inherent Trade-Off in Noisy Neural Communication with Rank-Order Coding

arXiv: https://arxiv.org/abs/2308.07034 · HTML: https://mlsanity.com/paper/2308.07034/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 14 Aug 2023.

## Builds on (most cited first, 4 of 4)

- [An Analytical Estimation of Spiking Neural Networks Energy Efficiency](https://mlsanity.com/paper/2210.13107/) (2022)
- [Brain-inspired global-local learning incorporated with neuromorphic computing](https://mlsanity.com/paper/2006.03226/) (2020)
- [MorphIC: A 65-nm 738k-Synapse/mm2 Quad-Core Binary-Weight Digital Neuromorphic Processor with Stochastic Spike-Driven Online Learning](https://mlsanity.com/paper/1904.08513/) (2019)
- [Self-consistent stochastic dynamics for finite-size networks of spiking neurons](https://mlsanity.com/paper/2112.14867/) (2021)

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

- [Hyperdimensional Decoding of Spiking Neural Networks](https://mlsanity.com/paper/2511.08558/) (2025)

## Abstract (arXiv)

Rank-order coding, a form of temporal coding, has emerged as a promising scheme to explain the rapid ability of the mammalian brain. Owing to its speed as well as efficiency, rank-order coding is increasingly gaining interest in diverse research areas beyond neuroscience. However, much uncertainty still exists about the performance of rank-order coding under noise. Herein we show what information rates are fundamentally possible and what trade-offs are at stake. An unexpected finding in this paper is the emergence of a special class of errors that, in a regime, increase with less noise.