Luca Ambrogioni

This paper introduces MDSI-AVI, a framework for modeling whole-brain causal interactions in fMRI data, revealing new insights into working memory mechanisms.

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.

The paper provides direct evidence of reversed visual hierarchy activation during mental imagery and reports an 11Hz oscillatory pattern during perception.

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.

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.