One dimensional approximations of neuronal dynamics reveal computational strategy.
One dimensional approximations of neuronal dynamics reveal computational strategy.
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DOI：
10.1371/journal.pcbi.1010784
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发表时间：
2023-01
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
神经元活动与其所体现的计算之间的关系仍然是一个悬而未决的问题。我们开发了一种新的方法，将观察到的神经元活动浓缩成定量准确，简单和可解释的模型，并在C中的单个神经元的不同系统和尺度上对其进行验证。到人类的功能磁共振成像。该模型将神经元活动视为互锁的一维轨迹的集合。尽管简单，但这些模型准确地预测了人类参与者未来的神经元活动和未来的决策。此外，由互连的天线形成的结构-脚手架-与系统的计算策略密切相关。我们使用这些支架来比较灵长类动物和在相同任务上训练的人工系统的计算策略，以确定人工智能体学习与灵长类动物相同策略的特定条件。使用我们的方法提取的计算策略预测新的刺激的具体错误。这些结果表明，我们的方法是一个强大的工具，研究计算和神经元活动之间的关系，在不同的系统。神经元成像技术的进步现在允许记录生物系统中相当一部分神经元。然而，如何从如此复杂的非线性时间序列数据中提取科学见解尚不清楚。在这里，我们开发了一种方法，用于直接解释几个不同的生物系统在各种实验室任务中使用的计算算法。这种方法的主要直觉是，为了在有噪声的动态系统中存储信息，必须保护信息不受噪声的影响。这个标准的结果在系统中，可以很好地近似的一维轨迹。这些轨迹直接映射到系统执行的计算，并且不会显著降低模型的定量准确性。我们将这种方法应用到几个不同的生物系统和人工神经网络。我们发现，生物系统倾向于找到问题的通用解决方案，而人工神经网络依赖于数据集的具体情况。
The relationship between neuronal activity and computations embodied by it remains an open question. We develop a novel methodology that condenses observed neuronal activity into a quantitatively accurate, simple, and interpretable model and validate it on diverse systems and scales from single neurons in C. elegans to fMRI in humans. The model treats neuronal activity as collections of interlocking 1-dimensional trajectories. Despite their simplicity, these models accurately predict future neuronal activity and future decisions made by human participants. Moreover, the structure formed by interconnected trajectories—a scaffold—is closely related to the computational strategy of the system. We use these scaffolds to compare the computational strategy of primates and artificial systems trained on the same task to identify specific conditions under which the artificial agent learns the same strategy as the primate. The computational strategy extracted using our methodology predicts specific errors on novel stimuli. These results show that our methodology is a powerful tool for studying the relationship between computation and neuronal activity across diverse systems. Advances in neuronal imaging techniques now allow for the recording of an appreciable fraction of neurons in biological systems. However, it is not clear how to extract scientific insight from such complex nonlinear time series data. Here we develop a methodology for the direct interpretation of the computational algorithms used by several distinct biological systems during a variety of laboratory tasks. The primary intuition of this methodology is that in order to store information in noisy dynamical systems, the information must be protected against noise. This criterion results in systems that can be well approximated by one-dimensional trajectories. These trajectories map directly to the computation performed by the system, and don’t dramatically reduce the quantitative accuracy of the models. We apply this method to several different biological systems and artificial neural networks. We find that biological systems tend to find generalistic solutions to problems, while artificial neural networks rely on specifics of the dataset.
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影响因子：
3.7
作者：
Dufour N;Redcay E;Young L;Mavros PL;Moran JM;Triantafyllou C;Gabrieli JD;Saxe R
通讯作者：
Saxe R
影响因子：
3.9
作者：
Gao, Yufei;Zhang, Yameng;Zhang, Jiacai
通讯作者：
Zhang, Jiacai
影响因子：
25
作者：
Laje, Rodrigo;Buonomano, Dean V.
通讯作者：
Buonomano, Dean V.
影响因子：
19.6
作者：
Ahamed, Tosif;Costa, Antonio C.;Stephens, Greg J.
通讯作者：
Stephens, Greg J.
DOI：
10.1073/pnas.0705546104
发表时间：
2007-11-20
影响因子：
11.1
作者：
Jones, Lauren M.;Fontanini, Alfredo;Katz, Donald B.
通讯作者：
Katz, Donald B.