Explore dynamic neural network structures through stochastic rewiring in this 47-minute lecture by Robert Legenstein from Graz University of Technology. Delve into computational theories of the brain, covering topics such as plasticity, noise, rewiring analysis, and reward-based learning. Examine the dynamics of parameters and initial learning processes in neural networks. Gain insights into the implications of stochastic rewiring for understanding brain function and developing more efficient artificial neural networks.
Dynamic Neural Network Structures Through Stochastic Rewiring
Overview
Syllabus
Introduction
Implications
plasticity
noise
Rewiring
Analysis
Rewardbased learning
Experiments
Dynamics of Parameters
Initial Learning
Conclusions
Questions
Taught by
Simons Institute
