Energy Costs and Neural Complexity Evolution in Changing Environments

Heesom-Green, Sian and Shock, Jonathan and Nitschke, Geoff (2025) Energy Costs and Neural Complexity Evolution in Changing Environments, Proceedings of 2025 Conference on Artificial Life (ALIFE 2025), Kyoto, Japan, MIT Press.

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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.

Item Type:	Conference paper
Subjects:	Computing methodologies > Artificial intelligence
Date Deposited:	13 Oct 2025 12:21
Last Modified:	13 Oct 2025 12:21
URI:	https://pubs.cs.uct.ac.za/id/eprint/1749

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