Title: Cooperation Is All You Need Show Chinese title

Title: 合作才是你所需要的全部

Abstract: 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. Weshow 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. Show Chinese abstract

Abstract: 超越“树突民主”，我们引入了“局部处理器的民主”，称为合作者。 在这里，我们将它们的能力与用于强化学习（RL）的排列不变神经网络中的应用进行了比较，同时也与基于Transformer的机器学习算法（如ChatGPT）进行了比较。 Transformer基于整合-发射“点”神经元的长期概念，而合作者则受到最近神经生物学突破的启发，这些突破表明精神生活的细胞基础依赖于新皮层中依赖上下文的锥体神经元，这些神经元有两个功能上不同的点。 我们展示出，当用于RL时，基于合作者的算法比基于Transformer的算法学习速度快得多，即使它们具有相同数量的参数。