Cooperation Is All You Need
Ahsan Adeel Junaid Muzaffar Khubaib Ahmed Mohsin Raza
Going beyond 'dendritic democracy', we introduce a 'democracy of local
processors', termed Cooperator. Here we compare their capabilities when used in
permutation-invariant neural networks for reinforcement learning (RL), with
machine learning algorithms based on Transformers, such as ChatGPT.
Transformers are based on the long-standing conception of integrate-and-fire
'point' neurons, whereas Cooperator is inspired by recent neurobiological
breakthroughs suggesting that the cellular foundations of mental life depend on
context-sensitive pyramidal neurons in the neocortex which have two
functionally distinct points. We show that when used for RL, an algorithm based
on Cooperator learns far quicker than that based on Transformer, even while
having the same number of parameters.