Finite-State Channel Models for Signal Transduction in Neural Systems

TL;DR

This paper explores how information theory can be applied to model and analyze intercellular signal transduction, especially ligand-receptor systems, with implications for understanding neural communication.

Contribution

It introduces a framework for applying finite-state channel models to biological signaling systems, bridging information theory and neuroscience.

Findings

Ligand-receptor systems can be modeled as finite-state channels.

Information-theoretic analysis provides insights into signal capacity and reliability.

Applications to neuroscience help understand neural communication mechanisms.

Abstract

Information theory provides powerful tools for understanding communication systems. This analysis can be applied to intercellular signal transduction, which is a means of chemical communication among cells and microbes. We discuss how to apply information-theoretic analysis to ligand-receptor systems, which form the signal carrier and receiver in intercellular signal transduction channels. We also discuss the applications of these results to neuroscience.