Energy Costs and Neural Complexity Evolution in Changing Environments
This work provides in silico support for biological theories on brain evolution and insights for energy-efficient robotic design, though it is incremental in applying existing methods to a new context.
This study evolved artificial neural networks (ANNs) in reinforcement learning agents to investigate how environmental variability and energy costs influence neural complexity, finding that under energy constraints, increasing seasonality led to smaller ANNs, challenging the Cognitive Buffer Hypothesis and supporting the Expensive Brain Hypothesis.
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.