Rodrigo Echeveste

This study analyzes sex-related biases in deep learning models for motor imagery BCIs, finding that performance disparities are mainly due to EEG pattern discriminability, not the models themselves.

This study develops a recurrent neural network model of a visual cortical hypercolumn optimized for sampling-based probabilistic inference, revealing how cortical dynamics arise from efficient computational functions.

The paper investigates how glucose availability affects synchronous activity in excitatory neural networks and finds that glucose is essential for sustaining high-frequency bursting, while lactate and pyruvate cannot substitute.