I specialize in Computational Cognitive Neuroscience and Neuro-AI. My research bridges the gap between biological and artificial intelligence, using fMRI data to ground AI models in human cognition.
My work focuses on two core areas:
Cognitive Planning: creating theory-driven, system-level AI models informed by neuroscience and psychology to emulate complex decision-making.
Visual Perception: developing generative decoding architectures that reconstruct visual stimuli from brain activity.
🎓 I have officially started my appointment as Visiting PhD Student at the Fiete Lab, MIT.
Feb 27, 2026
🎤 My abstract has been accepted for oral presentation at AIRMM2026. See you there on March 19-20!
Feb 04, 2026
💬 I’ll be at COSYNE2026 (March 12–15). If you’re around and would like to chat or discuss ideas, feel free to get in touch!
Sep 10, 2025
🎤 Presenting my work, “Evaluating Semantic Brain Regions Contribution to Visual Neural Decoding Performance on Different Classes of Stimuli”, at CIBB2025 in the special session “AI in Medical Imaging: Radiomics and Explainable Deep Learning”.
Objective. AI-based neural decoding reconstructs visual perception by leveraging generative models to map brain activity measured through functional magnetic resonance imaging (fMRI) into the observed visual stimulus. Approach. Traditionally, ridge linear models transform fMRI into a latent space, which is then decoded using variational autoencoders (VAE) or LDMs. Owing to the complexity and noisiness of fMRI data, newer approaches split the reconstruction into two sequential stages, the first one providing a rough visual approximation using a VAE, the second one incorporating semantic information through the adoption of LDM guided by contrastive language-image pre-training (CLIP) embeddings. This work addressed some key scientific and technical gaps of the two-stage neural decoding by: (1) implementing a gated recurrent unit-based architecture to establish a non-linear mapping between the fMRI signal and the VAE latent space, (2) optimizing the dimensionality of the VAE latent space, (3) systematically evaluating the contribution of the first reconstruction stage, and (4) analyzing the impact of different brain regions of interest (ROIs) on reconstruction quality. Main results. Experiments on the NSD, containing 73 000 unique natural images, along with fMRI of eight subjects, demonstrated that the proposed architecture maintained competitive performance while reducing the complexity of its first stage by 85%. The sensitivity analysis showcased that the first reconstruction stage is essential for preserving high structural similarity in the final reconstructions. Restricting analysis to semantic ROIs, while excluding early visual areas, diminished visual coherence, preserving semantics though. The inter-subject repeatability across ROIs was about 92% and 98% for visual and sematic metrics, respectively. Significance. This study represents a key step toward optimized neural decoding architectures leveraging non-linear models for stimulus prediction. Sensitivity analysis highlighted the interplay between the two reconstruction stages, while ROI-based analysis provided strong evidence that the two-stage AI model reflects the brain’s hierarchical processing of visual information.
@article{Veronese2025,author={Veronese, Lorenzo and Moglia, Andrea and Pecco, Nicolò and Anthony Della Rosa, Pasquale and Scifo, Paola and Mainardi, Luca and Cerveri, Pietro},journal={Journal of Neural Engineering},title={Optimized AI-based neural decoding from BOLD fMRI signal for analyzing visual and semantic ROIs in the human visual system},year={2025},issn={1741-2552},month=aug,number={4},pages={046048},volume={22},doi={10.1088/1741-2552/adfbc2},publisher={IOP Publishing},}
I am always open to discussing new collaborations or research ideas. Feel free to contact me!
