Francesco Fumarola

This paper models decision-making under uncertainty using Bayesian anchoring to explain the fourfold pattern of risk attitudes and related effects observed in experiments.

This study investigates how baseline modulations in recurrent neural networks control dynamical phases and achieve optimal performance without fine-tuning recurrent couplings.

The paper introduces a new model of excitatory-inhibitory networks that generate asynchronous activity without requiring large bias currents, supported by sparse neuron activity and high-variance synaptic weights.

The paper develops a framework to predict the effects of neural perturbations using functional causal flow inferred from resting state activity in monkeys.

The paper investigates how optogenetic and behavioral inputs modulate V1 circuit responses, showing that specific interneurons stabilize the circuit and locomotion affects excitatory cells through direct drive.