Chapter
The Physics of Emergent Stability in Networks
43:14 - 57:03 (13:48)
This podcast explores the physics of emergent stability in high dimensional networks and the insights gained from studying the dynamics of such systems, particularly in the field of neurobiology.
Clips
The podcast discusses the contribution of physics in the field of neurobiology and highlights the limitations of physics to explain the deterministic behavior of the universe.
43:14 - 48:31 (05:17)
Summary
The podcast discusses the contribution of physics in the field of neurobiology and highlights the limitations of physics to explain the deterministic behavior of the universe.
ChapterThe Physics of Emergent Stability in Networks
Episode#76 – John Hopfield: Physics View of the Mind and Neurobiology
PodcastLex Fridman Podcast
Attractor networks are dynamics where you can't be any, so you will soon find yourself on a well-determined pathway which goes somewhere.
48:31 - 51:21 (02:50)
Summary
Attractor networks are dynamics where you can't be any, so you will soon find yourself on a well-determined pathway which goes somewhere. This system can be thought of as just the motion of a point over time in a high dimensional space.
ChapterThe Physics of Emergent Stability in Networks
Episode#76 – John Hopfield: Physics View of the Mind and Neurobiology
PodcastLex Fridman Podcast
The study of Lyapunov functions and energy functions can help in understanding the emergent stability of high dimensional systems such as deep learning and neural networks.
51:24 - 57:03 (05:39)
Summary
The study of Lyapunov functions and energy functions can help in understanding the emergent stability of high dimensional systems such as deep learning and neural networks. This can provide insights into the characteristics and behavior of these complex systems.