A taxonomy of SNN training algorithms is presented with the release of NeuroTrain, an open benchmarking framework for reproducible comparisons across datasets and architectures.
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NERD uses RL-trained diffusion models on fMRI data to model higher-order uncertainty representations, outperforming controls and linking individual differences to neurofeedback success.
Neuromechanical digital twins embed neural controllers in simulated bodies to infer unmeasurable biophysical variables, generate testable hypotheses via perturbations, and bridge neuroscience with robotics and machine learning.
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NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Framework
A taxonomy of SNN training algorithms is presented with the release of NeuroTrain, an open benchmarking framework for reproducible comparisons across datasets and architectures.
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Characterizing higher-order representations through generative diffusion models explains human decoded neurofeedback performance
NERD uses RL-trained diffusion models on fMRI data to model higher-order uncertainty representations, outperforming controls and linking individual differences to neurofeedback success.
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The embodied brain: Bridging the brain, body, and behavior with neuromechanical digital twins
Neuromechanical digital twins embed neural controllers in simulated bodies to infer unmeasurable biophysical variables, generate testable hypotheses via perturbations, and bridge neuroscience with robotics and machine learning.