Ivilin Peev Stoianov

This study proposes an approach based on active inference with dynamic priors to infer dynamic intentions and perform tasks like trajectory prediction and object grasping.

This paper introduces a modeling approach to study embodied decision-making, showing that decision and action processes interact through feedback loops in active inference.

The paper analyzes neural dynamics in PPC areas during motor tasks, finding distinct population subspaces for planning and execution in different regions.

This study investigates how the brain encodes spatial intentions in area V6A during motor planning, revealing dynamic predictive encoding mechanisms.

This study investigates how embodied decision-making models adapt to environmental contingencies using active inference in a two-alternative forced choice task.

This study compares discrete and continuous models in a dynamic task, finding continuous models perform better with less resource use but less flexibility.

The paper introduces an Active Inference architecture for efficient control of complex kinematic chains by mapping extrinsic to intrinsic coordinates through inference.

This paper proposes an active inference framework for depth estimation that combines action-perception cycles and Predictive Coding principles to improve depth belief accuracy through biologically plausible neural circuits.

The paper presents a computational theory using Active Inference to model goal-directed actions, showing how the PPC and DVS support dynamic behavior through flexible intentions.

This paper proposes a hierarchical generative model of the hippocampal formation that explains generative replay and continual learning through spatial navigation and memory organization.