Neurons and Cognition
PulseAugur coverage of Neurons and Cognition — every cluster mentioning Neurons and Cognition across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
-
AI research papers tackle learning stability and RL complexity, while discourse heats up
Two recent arXiv papers explore the theoretical underpinnings of machine learning. One paper investigates the stability of learning algorithms when gradient information is sparse and intermittently available, suggesting…
-
New Interpretable Memory Model SBD Improves Spaced Repetition Accuracy
Researchers have developed SBD, a novel memory model for spaced repetition software that aims to improve accuracy and interpretability. Unlike existing models that rely solely on test set accuracy, SBD is designed to ex…
-
Paper reveals how input symmetries can distort AI representational similarity analyses
A new paper published on arXiv details how symmetries in network inputs can confound representational similarity analyses (RSMs). The research demonstrates that these stimulus symmetries can lead to functionally equival…
-
New cognitive model analyzes visuospatial complexity in embodied active vision
Researchers have introduced a new framework to analyze multimodal data, focusing on how complexity influences embodied perception and interaction in dynamic environments. This model categorizes complexity into quantitat…
-
New path integral model frames cognition as cost optimization
Researchers have developed a mathematical and physical formulation for cognitive cost optimization, modeling goal-directed cognitive processes as imaginary-time evolution under a projector Hamiltonian. This approach ali…
-
New EEGNet Architecture Improves Error Signal Decoding with Multisensory Feedback
Researchers have developed a novel multi-branch EEGNet architecture to improve the decoding of error-related potentials (ErrPs) under complex multisensory feedback conditions. This approach utilizes auxiliary supervisio…
-
Masked autoencoders learn perception-relevant neural representations from unlabeled data
Researchers have demonstrated that masked autoencoders can learn meaningful representations from unlabeled neural data, specifically resting-state neural activity. By pretraining a masked autoencoder on hours of spontan…
-
New AI model learns compact brain graph representations for cognitive state decoding
Researchers have developed a novel method for creating compact representations of functional brain graphs using a graph transformer autoencoder. This approach incorporates domain-specific geometric information as an ind…
-
Neuroscience paper proposes novel geometric framework for interbrain network analysis
Researchers have introduced a novel geometric framework for analyzing interbrain network dynamics in neuroscience. This approach moves beyond traditional correlation-based synchrony metrics by interpreting changes in ne…
-
REST-GAN model synthesizes EEG signals and learns transferable representations
Researchers have developed REST-GAN, a novel generative adversarial network designed to synthesize resting-state electroencephalogram (EEG) signals and extract transferable representations. This framework combines adver…
-
New framework simulates decoded neurofeedback using generative models
Researchers have developed DecNefSimulator, a new framework designed to simulate and analyze decoded neurofeedback (DecNef) processes. This tool utilizes generative models to act as virtual participants, allowing for th…
-
New Hybrid Model Learns Neuron Dynamics Using Neural ODEs
Researchers have developed a novel hybrid modeling framework that integrates neural ordinary differential equations (Neural ODEs) into biophysical neuron models. This approach allows for the flexible discovery of unknow…
-
Think-aloud data reshapes cognitive model discovery beyond behavior
Researchers have developed a new method for discovering cognitive models by incorporating "think aloud" traces alongside traditional behavioral data. This approach, applied to risky decision-making, significantly improv…