# Cooperation Is All You Need

arXiv: https://arxiv.org/abs/2305.10449 · HTML: https://mlsanity.com/paper/2305.10449/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 16 May 2023.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Attention Mechanisms in Computer Vision: A Survey](https://mlsanity.com/paper/2111.07624/) (2021)
- [The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning](https://mlsanity.com/paper/2109.02869/) (2021)
- [A comparison of partial information decompositions using data from real and simulated layer 5b pyramidal cells](https://mlsanity.com/paper/2206.06456/) (2022)
- [Context-sensitive neocortical neurons transform the effectiveness and efficiency of neural information processing](https://mlsanity.com/paper/2207.07338/) (2022)

## Built on by (most cited first, 3 of 3)

- [An Overlooked Role of Context-Sensitive Dendrites](https://mlsanity.com/paper/2408.11019/) (2024)
- [Beyond Attention: Toward Machines with Intrinsic Higher Mental States](https://mlsanity.com/paper/2505.06257/) (2025)
- [Scalable Machines with Intrinsic Higher Mental-State Dynamics](https://mlsanity.com/paper/2603.13453/) (2026)

## Abstract (arXiv)

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.