Spatially embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings
Spatially embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings
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DOI：
10.1038/s42256-023-00748-9
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发表时间：
2023-11-20
影响因子：
23.8
通讯作者：
Astle，Duncan E.
中科院分区：
文献类型：
--
作者：
Achterberg，Jascha;Akarca，Danyal;Astle，Duncan E.
大脑网络存在于资源有限的范围内。因此，大脑网络必须克服在其物理空间内发展和维持网络的代谢成本，同时执行其所需的信息处理。为了观察这些过程的效果，我们引入了空间嵌入递归神经网络(SeRNN)。SeRNN学习基本的任务相关推理，而存在于三维欧几里得空间，其中组成神经元的通信受到稀疏连接体的限制。我们发现seRNN收敛于结构和功能特征，这些特征也常见于灵长类大脑皮层。具体地说，他们使用模块化的小世界网络来收敛于求解推理，在这种网络中，功能相似的单元在空间上配置自己，以利用能量高效的混合选择代码。由于这些特征是一致出现的，seRNN揭示了许多常见的结构和功能大脑基序是紧密交织在一起的，可以归因于基本的生物优化过程。SeRNN将生物物理约束纳入一个完全人工的系统中，并可以作为结构和功能研究社区之间的桥梁，推动神经科学的理解向前发展。
Brain networks exist within the confines of resource limitations. As a result, a brain network must overcome the metabolic costs of growing and sustaining the network within its physical space, while simultaneously implementing its required information processing. Here, to observe the effect of these processes, we introduce the spatially embedded recurrent neural network (seRNN). seRNNs learn basic task-related inferences while existing within a three-dimensional Euclidean space, where the communication of constituent neurons is constrained by a sparse connectome. We find that seRNNs converge on structural and functional features that are also commonly found in primate cerebral cortices. Specifically, they converge on solving inferences using modular small-world networks, in which functionally similar units spatially configure themselves to utilize an energetically efficient mixed-selective code. Because these features emerge in unison, seRNNs reveal how many common structural and functional brain motifs are strongly intertwined and can be attributed to basic biological optimization processes. seRNNs incorporate biophysical constraints within a fully artificial system and can serve as a bridge between structural and functional research communities to move neuroscientific understanding forwards.