RevieweNeuro
A Tutorial for Information Theory in Neuroscience.
Review in eNeuro. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
111 citing papers in PubMed.
- Quantifying Information Stored in Synaptic Connections Rather Than in Firing Activities of Neural Networks.Neural computation · 2026Article
- The limitations of non-mechanistic methods for characterizing pathogen-pathogen interactions: A simulation study.PLoS computational biology · 2026Article
- Article
- Quantifying Correlogram Shape to Analyze Neuronal Firing Dynamics Recorded in TBI-on-a-Chip.Neuroinformatics · 2026Article
- Frontal Alpha Asymmetry and Electrodermal Activity: A Mutual Information Analysis Across Cognitive Load and Sleep Deprivation.Biosensors · 2026Article
- Synergistic and redundant information dynamics are modulated by Alzheimer's disease and cognitive impairment.bioRxiv : the preprint server for biology · 2026Article
- Evolution of object identity information in sensorimotor cortex throughout grasp.Nature communications · 2026Article
- Information theoretic measures of neural and behavioural coupling predict representational drift.PLoS computational biology · 2026Article
- Towards an informational account of interpersonal coordination.Nature reviews. Neuroscience · 2026Review
- Neural substrate of conditioned stimulus for associative learning in the hippocampus.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Revisiting alpha-theta cross-frequency dynamics during working memory.Cerebral cortex (New York, N.Y. : 1991) · 2026Article
- The Geometric Signatures of Brain State Transitions: Recursive Informational Curvature Reveals Hidden Dynamics in Primate Cortex.Neuroscience insights · 2026Article
- Emergent multiscale organisation of neural dynamics fragments in anaesthesia.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- The role of high-amplitude bursts of high-gamma activity in naturalistic speech and music listening.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Neurodatascience: Past, Present, and Future.Data science in science · 2026Article
- A corticospinal signature for interindividual pain sensitivity.Nature communications · 2025Article
- Direction of motion decoding in mouse V1: Neuron predictive power relates to functional connectivity organization.bioRxiv : the preprint server for biology · 2025Article
- Measuring Stimulus Information Transfer Between Neural Populations Through the Communication Subspace.Neural computation · 2025Article
- Consilience in Causation: Causal Emergence Is Found Across Measures of Causation.Entropy (Basel, Switzerland) · 2025Article
- The balance between integration and segregation drives network dynamics maximizing multistability and metastability.Scientific reports · 2025Article
51 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Understanding how neural systems integrate, encode, and compute information is central to understanding brain function. Frequently, data from neuroscience experiments are multivariate, the interactions between the variables are nonlinear, and the landscape of hypothesized or possible interactions between variables is extremely broad. Information theory is well suited to address these types of data, as it possesses multivariate analysis tools, it can be applied to many different types of data, it can capture nonlinear interactions, and it does not require assumptions about the structure of the underlying data (i.e., it is model independent). In this article, we walk through the mathematics of information theory along with common logistical problems associated with data type, data binning, data quantity requirements, bias, and significance testing. Next, we analyze models inspired by canonical neuroscience experiments to improve understanding and demonstrate the strengths of information theory analyses. To facilitate the use of information theory analyses, and an understanding of how these analyses are implemented, we also provide a free MATLAB software package that can be applied to a wide range of data from neuroscience experiments, as well as from other fields of study.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.