Alberto Pezzotta

This study examines age-related changes in neural and behavioural variability during a decision-making task in mice, finding increased neural variability and reduced variability quenching in specific brain regions.

This paper shows that RNNs learn abstract relational representations through low-rank recurrent dynamics, which support temporal abstraction and generalization.

The paper presents a brain-wide neural activity map from 621,733 neurons across 139 mice performing a decision-making task, showing how neural activity encodes task variables across brain regions.

The paper develops a method to engineer synthetic cis-regulatory elements that control gene expression in response to signals, showing they can be designed using quantitative rules.

This paper uses optimal control theory to model morphogen-driven tissue patterning, showing how cells can achieve precise and reproducible fate decisions despite noise and dynamic feedback.

The paper describes a modular data architecture for a neuroscience collaboration, enabling data contribution, access, and analysis across multiple labs.

This paper presents a network model that unifies recency and central tendency biases in working memory by showing how volatile memory content leads to contraction bias in rats and humans.

This paper develops a neural network model that reproduces free recall phenomena, including power laws and serial position effects, by incorporating firing rate adaptation, global inhibition, and heteroassociative learning.