Considerations in using recurrent neural networks to probe neural dynamics
Considerations in using recurrent neural networks to probe neural dynamics
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DOI：
10.1152/jn.00467.2018
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发表时间：
2019-12-01
影响因子：
2.5
通讯作者：
Kao, Jonathan C.
中科院分区：
文献类型：
--
作者：
Kao, Jonathan C.
递归神经网络(RNN)越来越多地被用来对行为动物执行的复杂认知和运动任务进行建模。RNN经过训练，在复制动物行为的同时，还捕获了经验记录的神经活动的关键统计数据。以这种方式，RNN可以被视为一个电子电路，其计算元素与它正在建模的大脑皮层区域共享相似的主题。此外，由于RNN的控制方程和参数是完全已知的，因此可以对它们进行分析，以提出关于神经种群如何计算的假设。在此背景下，我们提出了在延迟到达任务中使用RNN来模拟运动行为时的重要考虑因素。首先，通过改变网络的非线性激活和速率正则化，我们证明了复制单个神经元放电速率基元的RNN可能不能充分捕获重要的种群基元。其次，我们发现，即使当RNN在单个神经元和群体水平上复制关键的神经生理特征时，它们也可以通过截然不同的动力学机制做到这一点。为了区分这些机制，我们证明了与先前提出的动态机制一致的RNN对输入噪声具有更强的鲁棒性。最后，我们证明了这些动力学足以使RNN泛化到它没有训练过的任务。总之，这些结果强调了在使用RNN模型来探索神经动力学时的重要考虑因素。新和值得注意的人工神经元在递归神经网络(RNN)中可能类似于经验单位活动，但不能充分捕捉神经种群水平上的重要特征。RNN的动力学可以在低维投影中可视化，以提供对RNN的动力学机制的洞察。以不同方式训练的RNN可能会复制神经生理学主题，但机制截然不同。被训练为仅执行延迟到达任务的RNN可以泛化为执行目标被切换或目标位置被改变的任务。
Recurrent neural networks (RNNs) are increasingly being used to model complex cognitive and motor tasks performed by behaving animals. RNNs are trained to reproduce animal behavior while also capturing key statistics of empirically recorded neural activity. In this manner, the RNN can be viewed as an in silico circuit whose computational elements share similar motifs with the cortical area it is modeling. Furthermore, because the RNN's governing equations and parameters are fully known, they can be analyzed to propose hypotheses for how neural populations compute. In this context, we present important considerations when using RNNs to model motor behavior in a delayed reach task. First, by varying the network's nonlinear activation and rate regularization, we show that RNNs reproducing single-neuron firing rate motifs may not adequately capture important population motifs. Second, we find that even when RNNs reproduce key neurophysiological features on both the single neuron and population levels, they can do so through distinctly different dynamical mechanisms. To distinguish between these mechanisms, we show that an RNN consistent with a previously proposed dynamical mechanism is more robust to input noise. Finally, we show that these dynamics are sufficient for the RNN to generalize to tasks it was not trained on. Together, these results emphasize important considerations when using RNN models to probe neural dynamics.NEW & NOTEWORTHY Artificial neurons in a recurrent neural network (RNN) may resemble empirical single-unit activity but not adequately capture important features on the neural population level. Dynamics of RNNs can be visualized in low-dimensional projections to provide insight into the RNN's dynamical mechanism. RNNs trained in different ways may reproduce neurophysiological motifs but do so with distinctly different mechanisms. RNNs trained to only perform a delayed reach task can generalize to perform tasks where the target is switched or the target location is changed.