ÐÇ¿Õ´«Ã½ Neural Engineering SEMINAR
Speaker: Crispin Foli
Advisor: Prof. Bolu Ajiboye
Title: Wired for Action: Unraveling the Cognitive Computational Relevance of Brain Network Organization
Abstract:
Understanding the computational role of brain network organization is essential for unraveling the complexities of neural processing and cognitive function. In this seminar, we embark on a journey to explore the brain’s computational footprint in cognition, dissecting its complexity into three interconnected parts.
In the first part, we delve into the concept of the brain as a complex network. We examine how the intricate interconnections between neural elements form a network architecture that supports various cognitive processes. Drawing from network neuroscience principles, we discuss how the brain's structural and functional connectivity can be accurately described using complex network theory, shedding light on the brain's organizational principles.
Moving to the second part, we explore the mechanisms by which populations of neurons within a reference network learn to represent multiple tasks simultaneously. By exploring the principles of functional specialization, and dynamic reconfiguration, we gain insights into how the brain achieves remarkable flexibility in task representation.
In the final part, we focus on the computational role network connectivity plays in task representation. We investigate how cortical connectivity facilitates the integration of information across distributed neural networks, allowing for the seamless representation of multiple cognitive tasks. Through examples from cognitive neuroscience research, we highlight the importance of network connectivity in optimizing neural computations and supporting cognitive flexibility.
By elucidating the cognitive computational relevance of brain network organization, this seminar provides a comprehensive overview of how the brain efficiently processes information and executes diverse cognitive tasks. Our exploration of complex network dynamics and their computational implications advances our understanding of the neural basis of cognition.