2019/06/03 by Matthijs Meijers, Sosuke Ito, Pieter Rein ten Wolde
Biochemistry, Genetics and Molecular Biology · Mathematics · Neuroscience · Physics and Astronomy · #Advanced Thermodynamics and Statistical Mechanics #Artificial intelligence #Computer network #Computer science #Control theory (sociology) #Critical point (mathematics) #Criticality #Information flow #Information transmission #Ising model #Mathematical analysis #Mathematics #Monotonic function #Mutual information #Neural dynamics and brain function #Physics #Point (geometry) #SIGNAL (programming language) #Statistical physics #Telecommunications #Transmission (telecommunications) #cond-mat.soft #cond-mat.stat-mech #physics.bio-ph #q-bio.OT #stochastic dynamics and bifurcation
paper · pdf · doi:10.1103/physreve.103.l010102
published as Phys. Rev. E 103, 010102 (2021) · 16 pages, 13 figures
arxiv created 2019/06/03 · openalex publication_date 2021/01/27 · arxiv updated 2021/02/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Recent experiments have indicated that many biological systems self-organize near their critical point, which hints at a common design principle. While it has been suggested that information transmission is optimized near the critical point, it remains unclear how information transmission depends on the dynamics of the input signal, the distance over which the information needs to be transmitted, and the distance to the critical point. Here we employ stochastic simulations of a driven two-dimensional Ising system and study the instantaneous mutual information and the information transmission rate between a driven input spin and an output spin. The instantaneous mutual information varies nonmonotonically with the temperature but increases monotonically with the correlation time of the input signal. In contrast, there exists not only an optimal temperature but also an optimal finite input correlation time that maximizes the information transmission rate. This global optimum arises from a fundamental trade-off between the need to maximize the frequency of independent input messages, the necessity to respond fast to changes in the input, and the need to respond reliably to these changes. The optimal temperature lies above the critical point but moves toward it as the distance between the input and output spin is increased.