Energy Costs and Neural Complexity Evolution in Changing Environments

TL;DR

This study uses evolved neural networks in reinforcement learning agents to explore how environmental variability and energy costs influence the evolution of neural complexity, challenging traditional hypotheses about brain size evolution.

Contribution

It demonstrates how energy constraints and environmental seasonality affect neural network size and efficiency, providing computational evidence for biological theories of brain evolution.

Findings

Seasonality leads to smaller neural networks under energy constraints.

Energy costs promote the evolution of more efficient neural architectures.

Results support the Expensive Brain Hypothesis over the Cognitive Buffer Hypothesis.

Abstract

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.