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

TL;DR

This paper investigates the fundamental limits and trade-offs of noisy neural communication using rank-order coding, revealing an unexpected class of errors that increase as noise decreases, with implications for neuroscience and information theory.

Contribution

It characterizes the fundamental information rates and trade-offs in noisy rank-order neural coding, uncovering a novel class of errors that defy conventional expectations.

Findings

Identifies fundamental information rate limits for rank-order coding under noise.

Discovers a unique class of errors that increase with less noise.

Highlights the inherent trade-off between speed and accuracy in neural communication.

Abstract

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.