Analog biological backpropagation: A new conjecture "Self Aware Networks" explains how derivatives & loss functions are represented in the brain.
Also discussed is a comparison between analog computing and digital in the context of computational biology. In this video I am reading a recent discussion of my notes with Self Aware Networks.
Have you ever wanted to know how Gradient Descent could work in the real human brain? Here is a new conjecture that establishes a new argument about back propagation, derivatives, and loss functions in the brain with the purpose of connecting to ideas in Deep Neural Networks.
Watch the video here:
Read along with the video above via the text here:
https://github.com/v5ma/selfawarenetworks/blob/main/02san.md
If you just want the link to the video inside the video here it is:
A new book out today "Bridging Molecular Mechanisms and Neural Oscillatory Dynamics"
This book “Bridging Molecular Mechanisms and Neural Oscillatory Dynamics” and the Self Aware Networks Theory of Mind provide a novel unified framework for understanding consciousness addressing attention binding, the hard problem: how we observe what we observe, and qualia, what those observations are made out of .
A New Law of Thermodynamics
This letter came about because tonight I was watching Stephen Wolfram speak in a video to Dr. Brian Keating, and he was saying that the 2nd Law of Thermodynamics represents the observed result of an irreducible computation being performed by the molecules of gas. This made me sit up in my seat and realize that not only did I know the computation, being …
How Wave Perturbation & Dissipation Computation Could Explain Everything from Neurophysics to Astrophysics
Why does a steaming cup of coffee inevitably cool down? Why do the echoes of a loud sound fade over time? It’s all about wave perturbation & dissipation as a universal physics based computational process that might explain everything from the mind’s inner workings to the flow of energy across the cosmos.