Antonio Lozano

The paper identifies cell-type-specific spiking patterns during cortical ripples in macaque and human V1, showing conserved circuit mechanisms across species and conditions.

This paper develops a deep learning framework to control neural activity in the human visual cortex using a neuroprosthesis, achieving better performance than conventional methods.

The paper uses computational ethology tools to analyze behavioral responses to early life stress in mice, finding altered coping strategies and identifying resilient subpopulations with more diverse behaviors.

This paper develops an automatic method to optimize electrode placement for visual prostheses by matching phosphene distributions using retinotopic predictions and individual anatomy data.

This paper shows that feature-disentangled GAN latents better predict neural responses to visual stimuli than other representations, and can reconstruct visual perception with high fidelity.

This paper presents a differentiable phosphene simulator for optimizing visual cortical prostheses using biologically plausible models of visual perception.