论文标题
Looper：推断由神经元种群动态制定的计算算法
LOOPER: Inferring computational algorithms enacted by neuronal population dynamics
论文作者
论文摘要
现在可以记录数百个神经元的同时活动。现有方法可以建模这种人群活动，但不能直接揭示大脑使用的计算。我们提出了一种完全无监督的方法，该方法对神经元活动进行建模并揭示了计算策略。该方法构建了由互连环组成的神经元动力学的拓扑模型。回路之间的过渡标记计算上的决定。在工作记忆任务中，我们准确地模拟了灵长类化皮层中100s神经元的激活。在同一任务上训练的复发神经网络（RNN）的动力学在拓扑上是相同的，这表明使用了类似的计算策略。但是，在经过修改的数据集上训练的RNN揭示了不同的拓扑。该拓扑预测了特定的新型刺激，这些刺激始终以几乎完美的精度引起不正确的反应。因此，我们的方法论产生了神经元活动的定量模型，并揭示了用于解决任务的计算策略。
Recording simultaneous activity of hundreds of neurons is now possible. Existing methods can model such population activity, but do not directly reveal the computations used by the brain. We present a fully unsupervised method that models neuronal activity and reveals the computational strategy. The method constructs a topological model of neuronal dynamics consisting of interconnected loops. Transitions between loops mark computationally-salient decisions. We accurately model activation of 100s of neurons in the primate cortex during a working memory task. Dynamics of a recurrent neural network (RNN) trained on the same task are topologically identical suggesting that a similar computational strategy is used. The RNN trained on a modified dataset, however, reveals a different topology. This topology predicts specific novel stimuli that consistently elicit incorrect responses with near perfect accuracy. Thus, our methodology yields a quantitative model of neuronal activity and reveals the computational strategy used to solve the task.
