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We propose a model of parameter learning for signal transduction, where the objective function is defined by signal transmission efficiency. We apply this to learn kinetic rates as a form of evolutionary learning, and look for parameters…

Molecular Networks · Quantitative Biology 2014-08-12 Gabriele Scheler

We consider a living organism as an observer of the evolution of its environment recording sensory information about the state space X of the environment in real time. Sensory information is sampled and then processed on two levels. On the…

Artificial Intelligence · Computer Science 2010-03-22 Dan Guralnik

We consider the thermodynamic properties of systems in contact with an information source and focus on the consequences of energetic cost associated with the exchange of information. To this end we introduce the model of a thermal tape and…

Statistical Mechanics · Physics 2015-06-18 Johannes Hoppenau , Andreas Engel

Organisms use specialized sensors to measure their environments, but the fundamental principles that determine their accuracy remain largely unknown. In Escherichia coli chemotaxis, we previously found that gradient-climbing speed is…

Biological Physics · Physics 2025-08-28 Henry H. Mattingly , Keita Kamino , Jude Ong , Rafaela Kottou , Thierry Emonet , Benjamin B. Machta

When deploying artificial agents in real-world environments where they interact with humans, it is crucial that their behavior is aligned with the values, social norms or other requirements of that environment. However, many environments…

Machine Learning · Computer Science 2023-05-05 Mattijs Baert , Pietro Mazzaglia , Sam Leroux , Pieter Simoens

In this paper, a diffusion-based molecular communication channel between two nano-machines is considered. The effect of the amount of memory on performance is characterized, and a simple memory-limited decoder is proposed and its…

Biological Physics · Physics 2014-05-05 Reza Mosayebi , Hamidreza Arjmandi , Amin Gohari , Masoumeh Nasiri Kenari , Urbashi Mitra

The repertoire of neural activity patterns that a cortical network can produce constrains the network's ability to transfer and process information. Here, we measured activity patterns obtained from multi-site local field potential (LFP)…

Neurons and Cognition · Quantitative Biology 2010-12-17 Woodrow L. Shew , Hongdian Yang , Shan Yu , Rajarshi Roy , Dietmar Plenz

Cells often perform computations in response to environmental cues. A simple example is the classic problem, first considered by Berg and Purcell, of determining the concentration of a chemical ligand in the surrounding media. On general…

Molecular Networks · Quantitative Biology 2015-06-04 Pankaj Mehta , David J. Schwab

Despite the wide usage of information as a concept in science, we have yet to develop a clear & concise scientific definition. This paper is aimed at laying the foundations for a new theory concerning the mechanics of information alongside…

General Physics · Physics 2017-07-13 Kiyam Lin , SongLing Lin

The generation of spikes by neurons is energetically a costly process. This paper studies the consumption of energy and the information entropy in the signalling activity of a model neuron both when it is supposed isolated and when it is…

The metaphor of a potential epigenetic differentiation landscape broadly suggests that during differentiation a stem cell follows the steepest descending gradient toward a stable equilibrium state which represents the final cell type. It…

Cell Behavior · Quantitative Biology 2020-09-22 K. Wiesner , J. Teles , M. Hartnor , C. Peterson

A key tenet of the Transactional Interpretation of Quantum Mechanics is the idea that photon absorption localizes the absorbing material system. In doing so, it measures the location of the absorber and hence reduces information entropy,…

Quantum Physics · Physics 2024-12-31 Andreas Schlatter , R. E. Kastner

The information entropy budget and the rate of information transfer between variables is studied in the context of a nonlinear reduced-order atmospheric model. The key ingredients of the dynamics are present in this model, namely the…

Atmospheric and Oceanic Physics · Physics 2024-04-02 Stéphane Vannitsem , Carlos A. Pires , David Docquier

Living systems maintain or increase local order by working against the Second Law of Thermodynamics. Thermodynamic consistency is restored as they dissipate heat, thereby increasing the net entropy of their environment. Recently introduced…

Biological Physics · Physics 2021-05-03 Dominic J. Skinner , Jörn Dunkel

Here we characterized an information measure for cell polarity that applies to non-motile cells responding to a chemical gradient. The central idea is that polarization represents information about the direction of the gradient. We applied…

Cell Behavior · Quantitative Biology 2025-04-14 Tau-Mu Yi

The learning rate is an information-theoretical quantity for bipartite Markov chains describing two coupled subsystems. It is defined as the rate at which transitions in the downstream subsystem tend to increase the mutual information…

Statistical Mechanics · Physics 2017-07-04 Rory A. Brittain , Nick S. Jones , Thomas E. Ouldridge

We develop information theory for the temporal behavior of memoryful agents moving through complex -- structured, stochastic -- environments. We introduce and explore information processes -- stochastic processes produced by cognitive…

Statistical Mechanics · Physics 2025-08-04 James P. Crutchfield , Alexandra Jurgens

Scientific discovery can be framed as a thermodynamic process in which an agent invests physical work to acquire information about an environment under a finite work budget. Using established results about the thermodynamics of computing,…

Information Theory · Computer Science 2025-11-20 Mihir Rao

Bounded agents are limited by intrinsic constraints on their ability to process information that is available in their sensors and memory and choose actions and memory updates. In this dissertation, we model these constraints as…

Machine Learning · Computer Science 2017-03-31 Roy Fox

We address the fundamental limits of learning unknown parameters of any stochastic process from time-series data, and discover exact closed-form expressions for how optimal inference scales with observation length. Given a parametrized…

Machine Learning · Computer Science 2023-10-09 Paul M. Riechers
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