Mattia Rigotti

This study investigates how predictive learning in neural networks generates low-dimensional latent representations of sensory inputs by analyzing network dynamics and intrinsic dimensionality.

The paper examines how neural representations in monkeys and simulated networks encode abstract variables for generalization in decision-making tasks.

This paper shows that Hebbian learning in a random neural network can replicate mixed selectivity properties observed in prefrontal cortex neurons during complex tasks.