Mimicking human neuronal pathways in silico: an emergent model on the effective connectivity
Mimicking human neuronal pathways in silico: an emergent model on the effective connectivity
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DOI：
10.1007/s10827-013-0467-3
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发表时间：
2014-04-01
影响因子：
1.2
通讯作者：
Glize, Pierre
中科院分区：
文献类型：
--
作者：
Gurcan, Onder;Turker, Kemal S.;Glize, Pierre
我们提出了一种新的计算模型，检测给定的人类神经元通路的时间配置，并构建其人工复制。这是一个巨大的挑战，因为在人类中枢神经系统中不可能直接记录单个神经元，因此必须将潜在的神经元通路视为黑匣子。为了应对这一挑战，我们使用了复杂系统建模的一个分支，称为人工自组织，其中大量软件实体在本地交互会产生自下而上的集体行为。其结果是一个涌现模型，其中每个软件实体都代表一个集成并激发的神经元。然后，我们将该模型应用于从有意识的人类受试者获得的单个运动单元的反射反应。实验结果表明，该模型恢复真实的人类神经元通路的功能，通过比较它适当的替代数据。使该模型有希望的是，据我们所知，它是第一个通过有效地将神经科学与人工自组织相结合来自连接人工神经网络的现实模型。虽然目前还没有证据表明该模型的连接映射到人类的连接，但我们预计该模型将有助于神经科学家更多地了解人类神经元网络，也可以用于预测假设，以引导未来的实验。
We present a novel computational model that detects temporal configurations of a given human neuronal pathway and constructs its artificial replication. This poses a great challenge since direct recordings from individual neurons are impossible in the human central nervous system and therefore the underlying neuronal pathway has to be considered as a black box. For tackling this challenge, we used a branch of complex systems modeling called artificial self-organization in which large sets of software entities interacting locally give rise to bottom-up collective behaviors. The result is an emergent model where each software entity represents an integrate-and-fire neuron. We then applied the model to the reflex responses of single motor units obtained from conscious human subjects. Experimental results show that the model recovers functionality of real human neuronal pathways by comparing it to appropriate surrogate data. What makes the model promising is the fact that, to the best of our knowledge, it is the first realistic model to self-wire an artificial neuronal network by efficiently combining neuroscience with artificial self-organization. Although there is no evidence yet of the model's connectivity mapping onto the human connectivity, we anticipate this model will help neuroscientists to learn much more about human neuronal networks, and could also be used for predicting hypotheses to lead future experiments.