Shared Protentions in Multi-Agent Active Inference
The paper integrates Husserlian phenomenology, active inference in theoretical biology, and category theory to create a framework for understanding so…
Category: Scientific Research Papers
Active Inference and Intentional Behaviour
This paper explores how spontaneous learning in neuronal networks and cell cultures exemplifies emergent cognition and behavior. By employing the free…
Supervised structure learning
The paper explores supervised structure learning of discrete generative models by focusing on Bayesian model selection and data ingestion order. It em…
Generating meaning: active inference and the scope and limits of passive AI
The article explores the differences between generative models in living organisms and passive generative AI. It argues that while AI learns from data…
Relative representations for cognitive graphs
The paper discusses relative representations derived from latent spaces of neural networks, enabling comparison across various models. It explores the…
Integrating cognitive map learning and active inference for planning in ambiguous environments
The paper explores integrating cognitive map learning and active inference for navigation in ambiguous environments. It evaluates a clone-structured c…
Learning to Navigate from Scratch using World Models and Curiosity: the Good, the Bad, and the Ugly
The paper discusses a system combining world models and curiosity-driven exploration for autonomous navigation in unknown environments. It showcases e…
Path integrals, particular kinds, and strange things
The paper presents a path integral formulation of the free energy principle, detailing how particles evolve through time. It introduces a variational …
Bridging Cognitive Maps: a Hierarchical Active Inference Model of Spatial Alternation Tasks and the Hippocampal-Prefrontal Circuit
The article presents a hierarchical active inference model that integrates cognitive maps for navigation and task performance, focusing on the hippoca…
Brain-inspired computational intelligence via predictive coding
The paper discusses the limitations of traditional AI methods, particularly deep neural networks. It proposes predictive coding, a neuroscience-inspir…