5 papers
A Rate-Distortion Perspective on the Emergence of Number Sense in Unsupervised Generative Models
Leo D'Amato, Davide Nuzzi, Alberto Testolin +3
Number sense is a core cognitive ability supporting various adaptive behaviors and is foundational for mathematical learning. Here, we study its emergence in unsupervised generativ…
Dynamic planning in hierarchical active inference
Matteo Priorelli, Ivilin Peev Stoianov
By dynamic planning, we refer to the ability of the human brain to infer and impose motor trajectories related to cognitive decisions. A recent paradigm, active inference, brings f…
Deep hybrid models: infer and plan in a dynamic world
Matteo Priorelli, Ivilin Peev Stoianov
To determine an optimal plan for complex tasks, one often deals with dynamic and hierarchical relationships between several entities. Traditionally, such problems are tackled with…
Neural representation in active inference: using generative models to interact with -- and understand -- the lived world
Giovanni Pezzulo, Leo D'Amato, Francesco Mannella +4
This paper considers neural representation through the lens of active inference, a normative framework for understanding brain function. It delves into how living organisms employ…
Modeling motor control in continuous-time Active Inference: a survey
Matteo Priorelli, Federico Maggiore, Antonella Maselli +5
The way the brain selects and controls actions is still widely debated. Mainstream approaches based on Optimal Control focus on stimulus-response mappings that optimize cost functi…