These trending papers study biological and artificial networks used in machine learning and artificial intelligence. By simulating neural networks computationally, researchers seek to understand how complex behaviors emerge from the interactions of individual neurons and synapses. This leads to insights into brain function and potential applications in artificial intelligence and neural engineering.
Explore the following trending articles in Frontiers in Computational Neuroscience.
Alzheimer's disease lacks a cure, emphasizing the importance of early diagnosis. AI aids Alzheimer's diagnosis via MRI. SVM, RF, CNN, autoencoder, and transformer methods show promise in early detection, crucial due to lack of cure.
The Drift-Diffusion Model (DDM) elucidates decision-making by analyzing accuracy and response times. This review links DDM parameters to brain regions and explores their alteration in disorders like Parkinson's, ADHD, autism, OCD, and schizophrenia.
This study proposes an Active Inference-based theory for goal-directed actions in dynamic settings. Testing with an agent shows its effectiveness, suggesting the Posterior Parietal Cortex (PPC) as the neural locus.
This study reviews EEG-based emotion recognition, focusing on the detection of emotions and post-traumatic stress disorder (PTSD) using a novel approach.
Computational neuroscience advances from single-neuron to whole-brain models (WBMs), simulating brain-wide dynamics via white matter tracts, aiding understanding of diseases like Alzheimer's, Schizophrenia, and Epilepsy.
The review examines symmetry's role in optimal representations for AI and biological intelligence. It suggests symmetry-based representations enhance data efficiency, generalizability, and transferability in skill acquisition.
Goldman et al. unveil human consciousness dynamics with integrated models. Mean-field AdEx neuron modeling and connectome data demonstrate wake-sleep states, shedding light on brain responsiveness mechanisms.
This study uses a whole-brain model to analyze slow oscillations (SOs) during slow-wave sleep (SWS). It reveals SO emergence and propagation across the cortex, matching resting-state fMRI and sleep EEG data with realistic spatiotemporal patterns.
Recent CNS bioinformatics advances drive EEG-based brain–computer interface (BCI) progress. The review examines deep learning's role in motor imagery and seizure detection, outlining strengths, limitations, and real-world applications.
Integrated World Modeling Theory (IWMT) merges IIT and GNWT with Free Energy Principle and Active Inference. This article explores IWMT's philosophical and neural underpinnings, connecting predictive processing with machine learning.
Read other impactful articles published in Frontiers in Computational Neuroscience.