Introduction to Dynamics

COEX 

This introduction provides a concise overview of dynamic systems in neuroscience, emphasizing their significance and practical applications. It covers fundamental concepts and tools, examining dynamic behaviors across various degrees of freedom with examples from computational neuroscience. The course explores intricate mechanisms like criticality, bifurcations, and symmetry breaking, highlighting their roles in shaping complex behaviors in neuronal networks. It addresses self-organization phenomena within these networks, shedding light on the organizational principles governing neural systems. Additionally, the course makes the connection to probability density distributions, Free Energy, and the Maximum Information Principle. It elucidates the interplay of deterministic and stochastic forces, offering a nuanced understanding of their impact on neural dynamics.
In summary, this lecture delivers a comprehensive introduction to dynamical systems in neuroscience, enhancing our understanding of neural phenomena. It shall serve as a valuable resource for scholars, researchers and students studying the mechanisms of neural networks.