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.

翻译：超越“树突民主”，我们引入了一种名为“协作者”（Cooperator）的本地处理器民主机制。我们将其在用于强化学习（RL）的置换不变神经网络中的能力，与基于Transformer（如ChatGPT）的机器学习算法进行了比较。Transformer基于长期存在的“整合-发放”点神经元概念，而协作者则受到近期神经生物学突破的启发，该突破表明心理生活的细胞基础依赖于新皮层中具有两个功能不同位点的情境敏感性锥体神经元。我们证明，当用于强化学习时，基于协作者的算法学习速度远快于基于Transformer的算法，即使两者参数数量相同。