A Differential Topological Model for Olfactory Learning and Representation

TL;DR

This paper introduces a novel differential topological model for olfactory learning and representation, emphasizing geometric aspects over element-wise dynamics, and presents an organism-independent framework for olfactory processing.

Contribution

It develops a new geometric modeling approach for olfactory perception and constructs an organism-independent model, filling a gap in current literature.

Findings

New topological model for olfactory processing

Organism-independent framework established

Emphasis on geometric over element-wise dynamics

Abstract

This thesis is designed to be a self-contained exposition of the neurobiological and mathematical aspects of sensory perception, memory, and learning with a bias towards olfaction. The final chapters introduce a new approach to modeling focusing more on the geometry of the system as opposed to element wise dynamics. Additionally, we construct an organism independent model for olfactory processing: something which is currently missing from the literature.