# Energy Costs and Neural Complexity Evolution in Changing Environments

arXiv: https://arxiv.org/abs/2511.20018 · HTML: https://mlsanity.com/paper/2511.20018/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 50 references that are in the map.
- First posted on arXiv 25 Nov 2025.

## Builds on (most cited first, 2 of 2)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Measuring Integrated Information: Comparison of Candidate Measures in Theory and Simulation](https://mlsanity.com/paper/1806.09373/) (2018)

## Built on by (most cited first, 1 of 1)

- [Big Brains and Changing Environments: Cause or Consequence?](https://mlsanity.com/paper/2609.15569/) (2026)

## Abstract (arXiv)

The Cognitive Buffer Hypothesis (CBH) posits that larger brains evolved to enhance survival in changing conditions. However, larger brains also carry higher energy demands, imposing additional metabolic burdens. Alongside brain size, brain organization plays a key role in cognitive ability and, with suitable architectures, may help mitigate energy challenges. This study evolves Artificial Neural Networks (ANNs) used by Reinforcement Learning (RL) agents to investigate how environmental variability and energy costs influence the evolution of neural complexity, defined in terms of ANN size and structure. Results indicate that under energy constraints, increasing seasonality led to smaller ANNs. This challenges CBH and supports the Expensive Brain Hypothesis (EBH), as highly seasonal environments reduced net energy intake and thereby constrained brain size. ANN structural complexity primarily emerged as a byproduct of size, where energy costs promoted the evolution of more efficient networks. These results highlight the role of energy constraints in shaping neural complexity, offering in silico support for biological theory and energy-efficient robotic design.