# Connectionism, Complexity, and Living Systems: a comparison of Artificial and Biological Neural Networks

arXiv: https://arxiv.org/abs/2103.15553 · HTML: https://mlsanity.com/paper/2103.15553/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 57 references that are in the map.
- First posted on arXiv 15 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [How to represent part-whole hierarchies in a neural network](https://mlsanity.com/paper/2102.12627/) (2021)
- [Towards an integration of deep learning and neuroscience](https://mlsanity.com/paper/1606.03813/) (2016)
- [Increasing Generality in Machine Learning through Procedural Content Generation](https://mlsanity.com/paper/1911.13071/) (2019)
- [Analyzing biological and artificial neural networks: challenges with opportunities for synergy?](https://mlsanity.com/paper/1810.13373/) (2018)
- [Navigation of brain networks](https://mlsanity.com/paper/1801.07938/) (2018)
- [Do biological constraints impair dendritic computation?](https://mlsanity.com/paper/2103.03274/) (2021)

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

- [Meta-brain Models: biologically-inspired cognitive agents](https://mlsanity.com/paper/2109.11938/) (2021)
- [We Are All Creators: Generative AI, Collective Knowledge, and the Path Towards Human-AI Synergy](https://mlsanity.com/paper/2504.07936/) (2025)
- [Evolutionary Developmental Biology Can Serve as the Conceptual Foundation for a New Design Paradigm in Artificial Intelligence](https://mlsanity.com/paper/2506.12891/) (2025)

## Abstract (arXiv)

While Artificial Neural Networks (ANNs) have yielded impressive results in the realm of simulated intelligent behavior, it is important to remember that they are but sparse approximations of Biological Neural Networks (BNNs). We go beyond comparison of ANNs and BNNs to introduce principles from BNNs that might guide the further development of ANNs as embodied neural models. These principles include representational complexity, complex network structure/energetics, and robust function. We then consider these principles in ways that might be implemented in the future development of ANNs. In conclusion, we consider the utility of this comparison, particularly in terms of building more robust and dynamic ANNs. This even includes constructing a morphology and sensory apparatus to create an embodied ANN, which when complemented with the organizational and functional advantages of BNNs unlocks the adaptive potential of lifelike networks.