Cofré et al., 2025 - Google Patents
Entropy and Complexity Tools Across Scales in Neuroscience: A ReviewCofré et al., 2025
View HTML- Document ID
- 7896507576253144836
- Author
- Cofré R
- Destexhe A
- Publication year
- Publication venue
- Entropy
External Links
Snippet
Understanding the brain's intricate dynamics across multiple scales—from cellular interactions to large-scale brain behavior—remains one of the most significant challenges in modern neuroscience. Two key concepts, entropy and complexity, have been increasingly …
- 238000012552 review 0 title abstract description 22
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
- G06F19/322—Management of patient personal data, e.g. patient records, conversion of records or privacy aspects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/3437—Medical simulation or modelling, e.g. simulating the evolution of medical disorders
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/36—Computer-assisted acquisition of medical data, e.g. computerised clinical trials or questionnaires
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/22—Health care, e.g. hospitals; Social work
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Miraglia et al. | Brain connectivity and graph theory analysis in Alzheimer’s and Parkinson’s disease: the contribution of electrophysiological techniques | |
| Watts et al. | Machine learning’s application in deep brain stimulation for Parkinson’s disease: A review | |
| Mishra et al. | Dynamic functional connectivity of emotion processing in beta band with naturalistic emotion stimuli | |
| Rudroff | Revealing the complexity of fatigue: A review of the persistent challenges and promises of artificial intelligence | |
| Thammasan et al. | Cross-frequency power-power coupling analysis: A useful cross-frequency measure to classify ICA-decomposed EEG | |
| Sanchez et al. | Toward a new application of real-time electrophysiology: online optimization of cognitive neurosciences hypothesis testing | |
| Kato et al. | Utility of cognitive neural features for predicting mental health behaviors | |
| Si et al. | Brain network modeling based on mutual information and graph theory for predicting the connection mechanism in the progression of Alzheimer’s disease | |
| McClay et al. | A real-time magnetoencephalography brain-computer interface using interactive 3D visualization and the Hadoop ecosystem | |
| Martínez-Cancino et al. | What can local transfer entropy tell us about phase-amplitude coupling in electrophysiological signals? | |
| Omidvarnia et al. | On the spatial distribution of temporal complexity in resting state and task functional MRI | |
| Duma et al. | Grounding adaptive cognitive control in the intrinsic, functional brain organization: An HD-EEG resting state investigation | |
| Gutzen et al. | A modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets | |
| Zhang et al. | On the specificity and permanence of electroencephalography functional connectivity | |
| Akhonda et al. | Association of neuroimaging data with behavioral variables: a class of multivariate methods and their comparison using multi-task fMRI data | |
| Fan et al. | Lifespan development of the human brain revealed by large-scale network eigen-entropy | |
| Liu et al. | Which multivariate multi-scale entropy algorithm is more suitable for analyzing the EEG characteristics of mild cognitive impairment? | |
| Cofré et al. | Entropy and Complexity Tools Across Scales in Neuroscience: A Review | |
| Zhang et al. | Amortization transformer for brain effective connectivity estimation from fMRI data | |
| Senadheera et al. | Profiling Somatosensory Impairment after Stroke: Characterizing Common “Fingerprints” of Impairment Using Unsupervised Machine Learning-Based Cluster Analysis of Quantitative Measures of the Upper Limb | |
| Cano et al. | Assessing cognitive workload in motor decision-making through functional connectivity analysis: Towards early detection and monitoring of neurodegenerative diseases | |
| Pisarchik | Computational and mathematical methods for neuroscience | |
| Yao et al. | Exploring EEG emotion recognition through Complex networks: insights from the visibility graph of ordinal patterns | |
| Trujillo | K-th nearest neighbor (KNN) entropy estimates of complexity and integration from ongoing and stimulus-evoked electroencephalographic (EEG) recordings of the human brain | |
| Kritikos et al. | Can brain–computer interfaces replace virtual reality controllers? a machine learning movement prediction model during virtual reality simulation using eeg recordings |