研究发现环境越变化大，大脑越小，能量限制决定神经复杂度。
Energy Costs and Neural Complexity Evolution in Changing Environments
- 用强化学习神经网络模拟脑进化，测试能量与环境变化影响
- 季节性越强，网络越小，总能量摄入减少导致脑尺寸受限
- 结构复杂度是规模缩小的副产品，更节能才是关键优势
认知缓冲假说（CBH）认为更大的大脑有助于应对变化环境以提升生存率。然而，大脑越大代谢负担越重。除了脑容量，脑组织结构也影响认知能力，合适的架构或可缓解能量压力。本研究通过演化强化学习代理所使用的人工神经网络（ANN），探究环境变异性与能量成本如何影响神经复杂度（以网络大小和结构定义）。结果表明，在能量约束下，季节性增强导致更小的神经网络。这一发现挑战了CBH，支持昂贵大脑假说（EBH），因为高度季节性环境降低了净能量摄入，从而限制了脑尺寸。神经结构复杂度主要作为规模减小的副产物出现，能量成本促使演化出更高效的网络。这些结果凸显能量约束在塑造神经复杂度中的作用，为生物理论提供仿真支持，并对节能机器人设计具启发意义。
原文摘要 · Abstract (English)
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.
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