Radically Compositional Cognitive Concepts
This work addresses the challenge of modeling compositional cognitive concepts for computational neuroscience, offering a novel theoretical framework that could enhance interpretability and cross-disciplinary communication.
The paper tackles the problem that few models leverage the compositional structure of concepts, cognitive architecture, and mathematics by proposing a radically compositional approach using applied category theory. The result is a framework that overcomes complexity, improves interpretability, and provides a rigorous common language for scientific modeling, demonstrated through a case study translating compositional narrative concepts to neural circuits.
Despite ample evidence that our concepts, our cognitive architecture, and mathematics itself are all deeply compositional, few models take advantage of this structure. We therefore propose a radically compositional approach to computational neuroscience, drawing on the methods of applied category theory. We describe how these tools grant us a means to overcome complexity and improve interpretability, and supply a rigorous common language for scientific modelling, analogous to the type theories of computer science. As a case study, we sketch how to translate from compositional narrative concepts to neural circuits and back again.