Copertina

Using machine learning methods to predict cognitive age from psychophysiological tests

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Tyurina, Daria D., Stasenko, Sergey V., Lushnikov, Konstantin V., Vedunova, Maria V.
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Neurons and Cognition Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Computational Advances in Taste Perception: From Ion Channels to Neural Coding
di: Lazovsky, Vladimir A., et al.
Pubblicazione: (2025)

The shape of the brain's connections is predictive of cognitive performance: an explainable machine learning study
di: Lo, Yui, et al.
Pubblicazione: (2024)

Firing rate model for brain rhythms controlled by astrocytes
di: Stasenko, Sergey V., et al.
Pubblicazione: (2024)

Prediction horizon shapes representations in predictive learning
di: Ratzon, Aviv, et al.
Pubblicazione: (2025)

Criticality Analysis: Bio-inspired Nonlinear Data Representation
di: Scheper, Tjeerd V. olde
Pubblicazione: (2023)

Deep learning and whole-brain networks for biomarker discovery: modeling the dynamics of brain fluctuations in resting-state and cognitive tasks
di: Roffet, Facundo, et al.
Pubblicazione: (2024)

Astrocyte control bursting mode of spiking neuron network with memristor-implemented plasticity
di: Stasenko, Sergey V., et al.
Pubblicazione: (2023)

Cerebral microbleeds: Association with cognitive decline and pathology build-up
di: Rathore, Saima, et al.
Pubblicazione: (2024)

Fast weight programming and linear transformers: from machine learning to neurobiology
di: Irie, Kazuki, et al.
Pubblicazione: (2025)

Data Integration with Fusion Searchlight: Classifying Brain States from Resting-state fMRI
di: Wein, Simon, et al.
Pubblicazione: (2024)

Ensemble-based graph representation of fMRI data for cognitive brain state classification
di: Vlasenko, Daniil, et al.
Pubblicazione: (2025)

Multi-intention Inverse Q-learning for Interpretable Behavior Representation
di: Zhu, Hao, et al.
Pubblicazione: (2023)

Sparks of cognitive flexibility: self-guided context inference for flexible stimulus-response mapping by attentional routing
di: Sommers, Rowan P., et al.
Pubblicazione: (2025)

An introduction to reinforcement learning for neuroscience
di: Jensen, Kristopher T.
Pubblicazione: (2023)

Error correction in multiclass image classification of facial emotion on unbalanced samples
di: Lebedev, Andrey A., et al.
Pubblicazione: (2025)

Active learning of neural population dynamics using two-photon holographic optogenetics
di: Wagenmaker, Andrew, et al.
Pubblicazione: (2024)

Unsupervised learning of multiscale switching dynamical system models from multimodal neural data
di: Kim, DongKyu, et al.
Pubblicazione: (2025)

BCI-sift: An automated feature selection toolbox for Brain Computer Interface applications
di: Offenberg, Elena C, et al.
Pubblicazione: (2026)

Compositional meta-learning through probabilistic task inference
di: Bakermans, Jacob J. W., et al.
Pubblicazione: (2025)

Reward function compression facilitates goal-dependent reinforcement learning
di: Molinaro, Gaia, et al.
Pubblicazione: (2025)

Construction of a classification model for dementia among Brazilian adults aged 50 and over
di: Menezes, F. S., et al.
Pubblicazione: (2026)

Identifying Connectivity Distributions from Neural Dynamics Using Flows
di: Kim, Timothy Doyeon, et al.
Pubblicazione: (2026)

Riemannian Geometry for the classification of brain states with intracortical brain-computer interfaces
di: Marin-Llobet, Arnau, et al.
Pubblicazione: (2025)

Machine learning approaches to uncover the neural mechanisms of motivated behaviour: from ADHD to individual differences in effort and reward sensitivity
di: Trinh, Nam
Pubblicazione: (2026)

A ghost mechanism: An analytical model of abrupt learning in recurrent networks
di: Dinc, Fatih, et al.
Pubblicazione: (2025)

NLP4Neuro: Sequence-to-sequence learning for neural population decoding
di: Morra, Jacob J., et al.
Pubblicazione: (2025)

Separating the what and how of compositional computation to enable reuse and continual learning
di: Shan, Haozhe, et al.
Pubblicazione: (2025)

Consciousness is entailed by compositional learning of new causal structures in deep predictive processing systems
di: Aksyuk, V. A.
Pubblicazione: (2023)

Meta-learning In-Context Enables Training-Free Cross Subject Brain Decoding
di: Nan, Mu, et al.
Pubblicazione: (2026)

PySeizure: A single machine learning classifier framework to detect seizures in diverse datasets
di: Chybowski, Bartlomiej, et al.
Pubblicazione: (2025)

Cortex and subcortex play distinct roles over learning when cortical memory is limited
di: Farrell, Matthew, et al.
Pubblicazione: (2026)

HDGL: A hierarchical dynamic graph representation learning model for brain disorder classification
di: Jalali, Parnian, et al.
Pubblicazione: (2023)

Mapping minds not averages: a scalable subject-specific manifold learning framework for neuroimaging data
di: Geenjaar, Eloy, et al.
Pubblicazione: (2025)

Development and Validation of a Deep-Learning Model for Differential Treatment Benefit Prediction for Adults with Major Depressive Disorder Deployed in the Artificial Intelligence in Depression Medication Enhancement (AIDME) Study
di: Benrimoh, David, et al.
Pubblicazione: (2024)

Emergent time-keeping mechanisms in a deep reinforcement learning agent performing an interval timing task
di: Pednekar, Amrapali, et al.
Pubblicazione: (2025)

Deep multimodal saliency parcellation of cerebellar pathways: linking microstructure and individual function through explainable multitask learning
di: Tchetchenian, Ari, et al.
Pubblicazione: (2024)

Learning Glioblastoma Tumor Heterogeneity Using Brain Inspired Topological Neural Networks
di: Paul, Ankita, et al.
Pubblicazione: (2026)

ALVI Interface: Towards Full Hand Motion Decoding for Amputees Using sEMG
di: Kovalev, Aleksandr, et al.
Pubblicazione: (2025)

Emergence of hybrid computational dynamics through reinforcement learning
di: Kononov, Roman A., et al.
Pubblicazione: (2025)

OPTIMUS: Predicting Multivariate Outcomes in Alzheimer's Disease Using Multi-modal Data amidst Missing Values
di: Diaz, Christelle Schneuwly, et al.
Pubblicazione: (2025)