Phenomenological Perspectives on Neural Networks: A Deep Dive into Cognitive Mechanisms

Abstract

The purpose of this chapter is to introduce the interested reader to visually accessible, intuitive cognitive mechanisms behind the construction of artificial neural networks, aiming to bridge phenomenological perspectives to research in the area of deep learning. The chapter is almost self-contained; however, basic background of phenomenological perspective and terminology for AI neural networks is covered in the first section. A brief description of the phenomenological aspects of physical systems motivates the main technical background of the chapter, i.e., the notion of how, beyond the so-called "type-token abstraction", the type-level laws can give rise to forms, or functional constraints, on the representative entities, or tokens, culminating in some syntactic-semantic cross-talk. Due to the original way to approach the subject, it contains many unconventional and sensible behaviors of artificial and natural systems, a feature of the dynamical systems approach to physics. The objective of this chapter is to provide a comprehensive understanding to the enthusiastic reader about the visually accessible and intuitive cognitive mechanisms that underpin the development and structure of artificial neural networks. The ultimate goal is to establish a connection between the phenomenological perspectives and the extensive research conducted in the field of deep learning. This chapter offers an almost self-sufficient content; however, it does present a basic background of the phenomenological aspect and the terminology associated with AI neural networks in its initial section. Moreover, a concise portrayal of the phenomenological facets of physical systems serves as a driving force for the primary technical foundation elucidated within the chapter, which involves delving into the concept of how, surpassing the discrete "type-token abstraction," the laws at the type level have the potential to instigate the emergence of various forms and functional restrictions on the representative entities, or tokens. Consequently, this culminates in a substantial level of syntactic-semantic interaction. Owing to its innovative approach to the topic, the chapter encompasses numerous unconventional and rational behaviors exhibited by artificial and natural systems. This inherently stems from the dynamical systems perspective adopted within the realm of physics.