Abstract

Phenomenology, brain function, and dynamical neural networks intersect in the exploration of how subjective experience arises from neural dynamics. Phenomenology, as a philosophical framework, delves into the qualitative aspects of consciousness and subjective experience. Recent advancements in neuroscience have increasingly focused on understanding brain function through the lens of dynamical neural networks, which model the complex interactions among neurons and brain regions over time. This abstract explores the interface between phenomenology and dynamical neural networks, aiming to bridge philosophical inquiry with empirical neuroscience. It examines how dynamical neural networks can elucidate the neural mechanisms underlying various phenomenological phenomena such as perception, cognition, and emotion. Key concepts include the role of temporal dynamics, synchronization patterns, and network connectivity in shaping subjective experience and consciousness. By integrating insights from phenomenology with computational neuroscience approaches, this abstract discusses how dynamical neural network models provide a framework for investigating the neural correlates of consciousness and understanding the dynamics of brain function. The synergy between phenomenological inquiry and neural network modeling offers promising avenues for advancing our understanding of the mind-brain relationship and developing novel theoretical frameworks in cognitive neuroscience. This abstract sets the stage for interdisciplinary dialogue, emphasizing the potential of dynamical neural networks to elucidate the neurobiological basis of subjective experience and deepen our understanding of consciousness from both philosophical and empirical perspectives.