Paolo Del Giudice

This paper introduces a reinforcement learning agent that exhibits signal neutrality, scalar property, and collapsing boundaries, suggesting these are emergent properties of multi-timescale information processing in decision-making.

The paper modifies a generalized linear model to better capture non-stationary and non-linear spiking dynamics in neural networks using in-vitro data.

The paper applies density-based clustering to multi-channel neural data to analyze dynamic complexity and infer synaptic couplings in neural networks.