重复神经结构的命运
Fate of Duplicated Neural Structures
摘要：
统计力学根据成本效益平衡决定了物质不同排列的丰度。它的形式主义和现象学渗透到整个生物过程中，并为有效的计算设置了限制。在特定条件下，自我复制和计算复杂的模式变得更受欢迎，产生生命，认知和达尔文进化论。神经元和神经回路处于统计力学、计算和（通过它们在认知中的作用）自然选择之间的十字路口。我们能否建立神经回路的{ \em统计物理学}？这样的理论将告诉我们，在设定的能量、进化和计算条件下，我们会期待什么样的大脑。考虑到这一大局，我们专注于复制神经回路的命运。我们从中枢神经系统的例子来看，强调可能导致这种冗余的计算阈值。我们还研究了一个天真的成本效益平衡重复电路实现复杂的表型。由此，我们得出{ \em相图}和（类相位）之间的单一和重复的电路，限制复杂的认知进化路径的过渡。回到大局，类似的相图和转换可能会限制神经回路的I/O和内部连接模式。统计力学的形式主义似乎是这一有价值的研究路线的自然框架。
英文摘要：
Statistical mechanics determines the abundance of different arrangements of matter depending on cost-benefit balances. Its formalism and phenomenology percolate throughout biological processes and set limits to effective computation. Under specific conditions, self-replicating and computationally complex patterns become favored, yielding life, cognition, and Darwinian evolution. Neurons and neural circuits sit at a crossroads between statistical mechanics, computation, and (through their role in cognition) natural selection. Can we establish a {\em statistical physics} of neural circuits? Such theory would tell what kinds of brains to expect under set energetic, evolutionary, and computational conditions. With this big picture in mind, we focus on the fate of duplicated neural circuits. We look at examples from central nervous systems, with a stress on computational thresholds that might prompt this redundancy. We also study a naive cost-benefit balance for duplicated circuits implementing complex phenotypes. From this we derive {\em phase diagrams} and (phase-like) transitions between single and duplicated circuits, which constrain evolutionary paths to complex cognition. Back to the big picture, similar phase diagrams and transitions might constrain I/O and internal connectivity patterns of neural circuits at large. The formalism of statistical mechanics seems a natural framework for thsi worthy line of research.
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