José M. Sánchez‐Bornot

This paper introduces a metabolic cost asymmetry as a structural regularizer for recurrent spiking neural networks, showing it improves performance and reduces energy use.

This paper estimates the excitatory-inhibitory balance from ECoG data using Physics-Informed Neural Networks, finding reduced long-range and excitatory short-range connections under anesthesia.

This paper uses Physics-Informed Neural Networks to estimate directed functional connectivity from rsfMRI data, revealing sex-specific differences in autism and neurotypical brains.

This paper estimates parameters in brain dynamics models from resting-state fMRI data using Physics-Informed Neural Networks, revealing differences in brain network structure between neurotypical individuals and those with ASD.

This study introduces an adaptive signed random walk model to analyze brain networks with both positive and negative connections, showing it captures more interactions than classical methods.

This paper improves ASD classification using random walks-informed feature extraction and selection, achieving 76.54% accuracy with SVM.

This study compares neural responses to auditory stimuli between musicians and non-musicians using gamma-band entrainment, finding musicians show greater top-down control and non-musicians benefit from entrained gamma responses.

This paper introduces a cluster-permutation statistical method for high-dimensional MEG-based functional connectivity analysis, identifying hypersynchronization in MCI patients.