# A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization

arXiv: https://arxiv.org/abs/2310.15177 · HTML: https://mlsanity.com/paper/2310.15177/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 79 references that are in the map.
- First posted on arXiv 14 Oct 2023.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploration by Random Network Distillation](https://mlsanity.com/paper/1810.12894/) (2018)
- [Hopfield Networks is All You Need](https://mlsanity.com/paper/2008.02217/) (2020)
- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [Could a Large Language Model be Conscious?](https://mlsanity.com/paper/2303.07103/) (2023)
- [The Neural Coding Framework for Learning Generative Models](https://mlsanity.com/paper/2012.03405/) (2020)
- [Audio Transformers](https://mlsanity.com/paper/2105.00335/) (2021)
- [BeBold: Exploration Beyond the Boundary of Explored Regions](https://mlsanity.com/paper/2012.08621/) (2020)
- [Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations](https://mlsanity.com/paper/1810.07411/) (2018)
- [Brain-inspired Computational Intelligence via Predictive Coding](https://mlsanity.com/paper/2308.07870/) (2023)
- [Spiking Neural Predictive Coding for Continual Learning from Data Streams](https://mlsanity.com/paper/1908.08655/) (2019)
- [On the link between conscious function and general intelligence in humans and machines](https://mlsanity.com/paper/2204.05133/) (2022)
- [Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting](https://mlsanity.com/paper/1905.10696/) (2019)
- [Backprop-Free Reinforcement Learning with Active Neural Generative Coding](https://mlsanity.com/paper/2107.07046/) (2021)
- [Continual Competitive Memory: A Neural System for Online Task-Free Lifelong Learning](https://mlsanity.com/paper/2106.13300/) (2021)
- [Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators](https://mlsanity.com/paper/2206.01338/) (2022)
- [Backprop Diffusion is Biologically Plausible](https://mlsanity.com/paper/1912.04635/) (2019)
- [Maze Learning using a Hyperdimensional Predictive Processing Cognitive Architecture](https://mlsanity.com/paper/2204.00619/) (2022)
- [Towards a Predictive Processing Implementation of the Common Model of Cognition](https://mlsanity.com/paper/2105.07308/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Review of Neuroscience-Inspired Machine Learning](https://mlsanity.com/paper/2403.18929/) (2024)
- [From generative AI to the brain: five takeaways](https://mlsanity.com/paper/2511.16432/) (2025)

## Abstract (arXiv)

Over the last few years, large neural generative models, capable of synthesizing semantically rich passages of text or producing complex images, have recently emerged as a popular representation of what has come to be known as ``generative artificial intelligence'' (generative AI). Beyond opening the door to new opportunities as well as challenges for the domain of statistical machine learning, the rising popularity of generative AI brings with it interesting questions for Cognitive Science, which seeks to discover the nature of the processes that underpin minds and brains as well as to understand how such functionality might be acquired and instantianted in biological (or artificial) substrate. With this goal in mind, we argue that a promising research program lies in the crafting of cognitive architectures, a long-standing tradition of the field, cast fundamentally in terms of neuro-mimetic generative building blocks. Concretely, we discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition in terms of Hebbian adaptation operating in service of optimizing a variational free energy functional.