Chapter
Understanding how the brain learns and neuromorphic computing
1:18:01 - 1:26:59 (08:58)
The brain has an internal mechanism to adjust synapse weights to store useful information, which is similar to supervised learning in neuromorphic computing where the external user controls the hardware's synaptic weights. Growing new neurons doesn't seem to be the primary mechanism by which the brain learns.
Clips
This episode explores the concept of neuromorphic computing and how it ties into the workings of the brain.
1:18:01 - 1:22:44 (04:42)
Summary
This episode explores the concept of neuromorphic computing and how it ties into the workings of the brain. The focus is on the idea of supervised learning, where an external user can control synaptic weights to achieve desired network activity, as well as the concept of attractors and network dynamics.
ChapterUnderstanding how the brain learns and neuromorphic computing
Episode#225 – Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence
PodcastLex Fridman Podcast
Plasticity mechanisms in the brain extend beyond just weight update and can occur on shorter timescales than previously thought, including homeostasis adaptation and other yet to be discovered mechanisms.
1:22:44 - 1:26:59 (04:15)
Summary
Plasticity mechanisms in the brain extend beyond just weight update and can occur on shorter timescales than previously thought, including homeostasis adaptation and other yet to be discovered mechanisms. These discoveries could lead to improvements in machine learning algorithms and a better understanding of how the brain learns and adapts.