[Paper Review] Cooperation Is All You Need

Ahsan Adeel, Junaid Muzaffar+6 more|arXiv (Cornell University)|May 16, 2023

Neural dynamics and brain functionNeuroscience3 citations

TL;DR

This paper introduces Cooperator, a cooperative neural architecture inspired by context-sensitive two-point pyramidal neurons in the neocortex, which outperforms Transformer-based models in permutation-invariant reinforcement learning tasks. Despite identical parameter counts, Cooperator achieves significantly faster learning and higher fitness scores in CartPole and PyBullet Ant environments by using contextual feedback to suppress irrelevant feedforward signals, enabling more efficient and resilient information processing.

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.

Motivation & Objective

• To address the inefficiency of point-neuron-based models in deep learning, which transmit all inputs regardless of relevance.
• To investigate whether context-sensitive neural processing—inspired by two-point pyramidal neurons—can outperform standard attention mechanisms in reinforcement learning.
• To demonstrate that cooperation among local processors, guided by contextual feedback, improves learning speed and stability in permutation-invariant RL agents.
• To challenge the dominance of 'dendritic democracy' in deep networks by proposing a 'democracy of local processors' (DoLP) that prioritizes contextual alignment over raw input integration.

Proposed method

• Cooperator implements a two-point neuron model where basal dendrites receive feedforward inputs and apical dendrites integrate contextual signals from neighboring neurons.
• The context-sensitive processor computes a contextual integration zone (C) as an average opinion of neighboring neurons, which modulates the transmission of feedforward signals.
• Transmission probability is amplified when the neuron's perception aligns with the majority of its neighbors, suppressing conflicting or irrelevant inputs.
• The architecture uses a single layer of such context-sensitive neurons followed by a simple policy network, maintaining the same number of parameters as baseline Transformers.
• The model employs burst-dependent synaptic plasticity in spiking simulations to enhance learning efficiency and resilience.
• Permutation invariance is preserved by using attention-like mechanisms that process inputs without relying on fixed order, while leveraging contextual cooperation.

Experimental results

Research questions

• RQ1 Can a cooperative, context-sensitive neural architecture outperform standard Transformers in reinforcement learning despite identical parameter counts?
• RQ2 How does context-sensitive processing in two-point neurons improve learning speed and reduce noise compared to traditional integrate-and-fire point neurons?
• RQ3 To what extent does contextual alignment among local processors enhance information transmission efficiency and reduce energy consumption in deep networks?
• RQ4 Does the proposed DoLP mechanism—where neurons cooperate to suppress irrelevant signals—lead to faster convergence and lower variance in RL training?

Key findings

• In the CartPole environment, Cooperator achieved a fitness score of 538 ± 419 after 10,000 episodes, significantly outperforming the Transformer’s 340 ± 310.
• For shuffled inputs, Cooperator reached 508 ± 408 at 5,000 episodes, while the Transformer remained below 350, demonstrating superior robustness to input order changes.
• In the PyBullet Ant task, Cooperator achieved 1,170 ± 35 fitness points after 1,000 episodes, compared to the Transformer’s 121 ± 53, showing a dramatic performance gap.
• Cooperator reduced training variance and achieved higher fitness scores faster than the baseline, even when input order was randomized.
• The model’s performance was stable and consistent across both shuffled and unshuffled input scenarios, indicating strong generalization and robustness.
• The results support the hypothesis that context-sensitive, cooperative processing reduces neural activity and energy use while enhancing learning efficiency and resilience.