Environment and Behavior Influence the Complexity of Evolved Neural Networks
Environment and Behavior Influence the Complexity of Evolved Neural Networks
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DOI：
10.1177/105971230401200103
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发表时间：
2004-03
影响因子：
1.6
通讯作者：
A. Seth;G. Edelman
中科院分区：
文献类型：
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作者：
A. Seth;G. Edelman
环境结构如何影响适应性行为的动态及其潜在机制？通过分析模拟头/眼系统的神经控制器，我们证明了一种特定的测量方法——“神经复杂性”——可以选择性地敏感于丰富适应行为背后的神经动力学。采用进化算法生成神经网络控制器，使其能够在不同复杂度的环境和表型条件下支持目标固定。与在简单条件下进化的网络相比，在丰富条件下进化的网络表现出更高的行为灵活性和鲁棒性，以及更高的神经复杂性。神经复杂性的大小反映了动态整合和动态分离之间的平衡，取决于环境和头/眼表型的特性。这些结果表明，在丰富的环境和表型条件下，神经复杂的动态可以伴随适应性行为；它们与神经复杂性可能代表自适应神经系统功能组织的共同特性的建议是一致的。
How does environmental structure influence the dynamics of adaptive behavior and its underlying mechanisms? By analyzing the neural controller of a simulated head/eye system, we show that a specific measure—“neural complexity”—can be selectively sensitive to neural dynamics underlying rich adaptive behavior. Evolutionary algorithms were used to generate neural network controllers able to support target fixation in environmental and phenotypic conditions of qualitatively different complexity. Networks that evolved in rich conditions showed higher behavioral flexibility and robustness, and higher neural complexity, than networks that evolved in simple conditions. The magnitude of neural complexity, which reflects a balance between dynamical integration and dynamical segregation, depended on properties of both the environment and the head/eye phenotype. These results show that neurally complex dynamics can accompany adaptive behavior in rich environmental and phenotypic conditions; they are consistent with the proposal that neural complexity may represent a common property of the functional organization of adaptive neural systems.