Interrogating theoretical models of neural computation with emergent property inference.
Interrogating theoretical models of neural computation with emergent property inference.
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DOI：
10.7554/elife.56265
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发表时间：
2021-07-29
期刊：
影响因子：
7.7
通讯作者：
Cunningham J
中科院分区：
文献类型：
--
作者：
Bittner SR;Palmigiano A;Piet AT;Duan CA;Brody CD;Miller KD;Cunningham J
理论神经科学的基石是电路模型：一个捕捉假设神经机制的方程系统。当这些模型产生实验观察到的现象时，无论是行为还是神经活动模式，它们都是有价值的，因此可以提供对神经计算的见解。这些电路的操作，像所有的模型，关键取决于模型参数的选择。然后，关键的一步是识别与观察到的现象相一致的模型参数：求解逆问题。在这项工作中，我们提出了一种新的技术，涌现属性推理（EPI），带来了现代概率建模工具包的理论神经科学。当建立电路模型时，理论家主要关注于再现计算特性，而不是特定的数据集。我们的方法使用深度神经网络来学习具有这些计算属性的参数分布。这种方法是通过在口胃神经节的参数推断动机的例子。EPI，然后示出，以允许精确的控制推断参数的行为，并在参数尺寸比替代技术更好地缩放。在剩下的工作中，我们提出了新的理论发现，初级视皮层和上级丘，这是通过检查复杂的参数结构捕获EPI模型。除了其科学贡献之外，这项工作还说明了一旦深度学习被用于解决理论逆问题，可能会出现的各种分析。
A cornerstone of theoretical neuroscience is the circuit model: a system of equations that captures a hypothesized neural mechanism. Such models are valuable when they give rise to an experimentally observed phenomenon -- whether behavioral or a pattern of neural activity -- and thus can offer insights into neural computation. The operation of these circuits, like all models, critically depends on the choice of model parameters. A key step is then to identify the model parameters consistent with observed phenomena: to solve the inverse problem. In this work, we present a novel technique, emergent property inference (EPI), that brings the modern probabilistic modeling toolkit to theoretical neuroscience. When theorizing circuit models, theoreticians predominantly focus on reproducing computational properties rather than a particular dataset. Our method uses deep neural networks to learn parameter distributions with these computational properties. This methodology is introduced through a motivational example of parameter inference in the stomatogastric ganglion. EPI is then shown to allow precise control over the behavior of inferred parameters and to scale in parameter dimension better than alternative techniques. In the remainder of this work, we present novel theoretical findings in models of primary visual cortex and superior colliculus, which were gained through the examination of complex parametric structure captured by EPI. Beyond its scientific contribution, this work illustrates the variety of analyses possible once deep learning is harnessed towards solving theoretical inverse problems.