Gaia Tavoni

The paper infers the structure of a neuronal circuit in the orbitofrontal cortex involved in economic decisions using network analysis and finds a reproducible, state-dependent network structure.

This study estimates memory capacity in heterogeneous neural networks and finds that maximizing capacity requires correlations between inward connections and coding levels in brain-inspired models.

This paper develops a unified theory showing how context-conditioned efficient coding and predictive coding are mathematically equivalent, explaining diverse neural phenomena through a single framework.

The paper develops a large-scale computational model of the olfactory bulb network, showing how connectivity and dynamics shape odor processing and perception.

The paper proposes a statistical mechanism for how context changes odor perception through feedback and cortical gating, predicting convergence and divergence trends based on odor similarity.

This paper shows model-free and model-based learning strategies can be equally flexible and effective, challenging the assumption that model-based strategies are inherently more complex.

The paper investigates the balance between complexity and accuracy in inference strategies and finds a complexity dividend in moderately uncertain environments.