×
Well done. You've clicked the tower. This would actually achieve something if you had logged in first. Use the key for that. The name takes you home. This is where all the applicables sit. And you can't apply any changes to my site unless you are logged in.

Our policy is best summarized as "we don't care about _you_, we care about _them_", no emails, so no forgetting your password. You have no rights. It's like you don't even exist. If you publish material, I reserve the right to remove it, or use it myself.

Don't impersonate. Don't name someone involuntarily. You can lose everything if you cross the line, and no, I won't cancel your automatic payments first, so you'll have to do it the hard way. See how serious this sounds? That's how serious you're meant to take these.

×
Register


Required. 150 characters or fewer. Letters, digits and @/./+/-/_ only.
  • Your password can’t be too similar to your other personal information.
  • Your password must contain at least 8 characters.
  • Your password can’t be a commonly used password.
  • Your password can’t be entirely numeric.
Enter the same password as before, for verification.
Login

Grow A Dic
Define A Word
Make Space
Set Task
Mark Post
Apply Votestyle
Create Votes
(From: saved spaces)
Exclude Votes
Apply Dic
Exclude Dic

Learning in associative networks through Pavlovian dynamics

Click here to flash read.

arXiv:2405.03823v1 Announce Type: cross
Abstract: Hebbian learning theory is rooted in Pavlov's Classical Conditioning. In addition, while mathematical models of the former have been proposed and studied in the past decades, especially in spin glass theory, only recently it has been numerically shown that the Pavlovian neural and synaptic dynamics mechanisms give rise to synaptic weights that correspond to the Hebbian learning rule. In this paper, we show that we can analytically prove this result. Moreover, we obtain the Pavlovian dynamics directly with statistical mechanics considerations, so that we can model the whole process within the same framework. Then, we test and study the model in different numerical settings. Finally, drawing from evidence on pure memory reinforcement during sleep stages, we show how the proposed model can simulate neural networks that undergo sleep-associated memory consolidation processes, thereby proving the compatibility of Pavlovian learning.

Click here to read this post out
ID: 843054; Unique Viewers: 0
Unique Voters: 0
Total Votes: 0
Votes:
Latest Change: May 8, 2024, 7:32 a.m. Changes:
/u/anonymous
Dictionaries:
Words:
Spaces:
Views: 6
CC:
No creative common's license
Comments: