Stefano Anzellotti

Face-selective responses correlate with deep networks that learn from environment feedback

The paper introduces a reinforcement learning model for face perception that incorporates environmental feedback, showing it matches supervised and unsupervised models in capturing neural responses to faces.

DeepCor: Denoising fMRI Data with Contrastive Autoencoders

DeepCor improves fMRI denoising by using contrastive autoencoders, outperforming CompCor on simulated data and enhancing connectivity differences in real datasets.

Beyond linear regression: mapping models in cognitive neuroscience should align with research goals

The paper argues that mapping models in cognitive neuroscience should be chosen based on research goals rather than being strictly linear or nonlinear, and introduces complexity metrics for evaluation.

PyMVPD: A toolbox for multivariate pattern dependence

PyMVPD is a toolbox for modeling multivariate pattern dependence between brain regions, showing that neural networks outperform linear regression and that model performance varies by brain region.

Computational Fingerprints: Modeling Interactions Between Brain Regions as Points in a Function Space

This paper introduces computational fingerprints to model nonlinear brain region interactions using fMRI data, showing they detect statistical dependence even when linear correlations are absent.

Intersubject MVPD: Empirical Comparison of fMRI Denoising Methods for Connectivity Analysis

This paper evaluates fMRI denoising methods using real data by comparing standard and intersubject multivariate pattern dependence metrics to assess their effectiveness.

Multivariate Pattern Connectivity

This paper introduces and applies multivariate pattern connectivity (MVPC) to study brain region interactions by analyzing fine-grained response patterns, revealing new connections not detected by univariate methods.

Measuring and Modeling Nonlinear Interactions Between Brain Regions with fMRI

This study introduces a framework using neural networks to model nonlinear interactions between brain regions in fMRI data, showing improved accuracy and revealing task-dependent functional subdivisions.

Multimodal representations of person identity individuated with fMRI

This study shows that the posterior superior temporal sulcus encodes person identity information invariantly across visual and auditory modalities.