Understanding the emergence of modularity in neural systems
Understanding the emergence of modularity in neural systems
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DOI：
10.1080/15326900701399939
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发表时间：
2007-07-01
影响因子：
2.5
通讯作者：
Bullinaria, John A.
中科院分区：
文献类型：
--
作者：
Bullinaria, John A.
人类大脑中的模块化仍然是一个有争议的问题，对存在的模块的性质，以及它们为什么，何时以及如何出现存在分歧。模块化提供了某种形式的计算优势，这是一个自然的假设，因此自然选择的进化已经将这些优势转化为认知科学家熟悉的模块化神经结构。然而，对简化神经系统进化的模拟表明，在许多情况下，非模块化架构实际上是最有效的。在本文中，相关的问题进行了讨论，并提出了一系列的模拟，揭示了关键的依赖关系的学习算法和正在建模的任务的细节，并考虑到已知的物理大脑的限制，如神经连接的程度的重要性。一个模式的建立提供了一个解释，为什么模块化应该出现可靠的范围内的神经处理任务。
Modularity in the human brain remains a controversial issue, with disagreement over the nature of the modules that exist, and why, when, and how they emerge. It is a natural assumption that modularity offers some form of computational advantage, and hence evolution by natural selection has translated those advantages into the kind of modular neural structures familiar to cognitive scientists. However, simulations of the evolution of simplified neural systems have shown that, in many cases, it is actually non-modular architectures that are most efficient. In this paper, the relevant issues are discussed and a series of simulations are presented that reveal crucial dependencies on the details of the learning algorithms and tasks that are being modelled, and the importance of taking into account known physical brain constraints, such as the degree of neural connectivity. A pattern is established which provides one explanation of why modularity should emerge reliably across a range of neural processing tasks.