Yağmur Güçlütürk

This study develops an end-to-end framework to learn safe stimulation parameters for cortical neuroprosthetic vision, improving phosphene interpretability while maintaining image reconstruction performance.

This paper introduces PAM, a predictive attention mechanism for neural decoding of visual perception, achieving state-of-the-art reconstructions and providing interpretative insights through attention weights and values.

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.

The paper introduces Brain2Pix, a method to reconstruct naturalistic videos from brain activity using fully convolutional networks and retinotopic mappings, showing significant improvement over existing techniques.

This paper reconstructs faces from fMRI data using GAN latent spaces, achieving accurate perception reconstructions by aligning neural responses with generator features.

The paper introduces a framework for system identification in neuroscience using cortical information flow, demonstrating its ability to estimate meaningful neural computations from fMRI data.

DeepRF uses deep learning to rapidly estimate population receptive fields, outperforming traditional methods in speed and accuracy.

This paper uses generative adversarial networks to reconstruct natural images from brain activity, achieving 67.2% and 66.4% identification rates in behavioral tests.