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Found 8 papers, 0 papers with code
Neural representation in active inference: using generative models to interact with -- and understand -- the lived world
This paper considers neural representation through the lens of active inference, a normative framework for understanding brain function.
Learning Spatial and Temporal Hierarchies: Hierarchical Active Inference for navigation in Multi-Room Maze Environments
Cognitive maps play a crucial role in facilitating flexible behaviour by representing spatial and conceptual relationships within an environment.
Bridging Cognitive Maps: a Hierarchical Active Inference Model of Spatial Alternation Tasks and the Hippocampal-Prefrontal Circuit
Through a series of simulations, we demonstrate that the model's dual layers acquire effective cognitive maps for navigation within physical (HC map) and task (mPFC map) spaces, using a biologically-inspired approach: a clone-structured cognitive graph.
Integrating cognitive map learning and active inference for planning in ambiguous environments
Living organisms need to acquire both cognitive maps for learning the structure of the world and planning mechanisms able to deal with the challenges of navigating ambiguous environments.
Inferring Hierarchical Structure in Multi-Room Maze Environments
Cognitive maps play a crucial role in facilitating flexible behaviour by representing spatial and conceptual relationships within an environment.
Object-Centric Scene Representations using Active Inference
Representing a scene and its constituent objects from raw sensory data is a core ability for enabling robots to interact with their environment.
Disentangling Shape and Pose for Object-Centric Deep Active Inference Models
Recently, deep learning methods have been proposed to learn a hidden state space structure purely from data, alleviating the experimenter from this tedious design task, but resulting in an entangled, non-interpreteable state space.
Disentangling What and Where for 3D Object-Centric Representations Through Active Inference
In this paper, we propose an active inference agent that actively gathers evidence for object classifications, and can learn novel object categories over time.
