Umut Güçlü

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 study uses Bayesian optimization to find optimal stimulation patterns for cortical neuroprosthetics, improving phosphene perception quality through patient feedback.

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.

The paper identifies neural states in fMRI data that align with environmental changes, supporting the idea that neural states reflect stable environmental features and follow a cortical temporal hierarchy.

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.

The paper investigates how neural states are organized across the cortical hierarchy and their relation to perceived event boundaries in the human brain.

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

This paper introduces methods to detect neural state transitions linked to event segmentation and shows they outperform existing methods.

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.

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

The paper introduces two nonparametric Bayesian methods for estimating spike-spike and spike-membrane neuronal connectivity using Gaussian process regression and validates them on simulated and real data.

The paper uses CNNs to model visual object recognition in MEG data, showing a feedforward sweep across the visual hierarchy and achieving high decoding accuracy.