论文标题
决策过程中神经动力学的统一模型
Unifying and generalizing models of neural dynamics during decision-making
论文作者
论文摘要
系统和计算神经科学中的一个开放问题是神经回路如何积累证据以实现决策。将决策理论的模型拟合到神经活动有助于回答这个问题，但是当前的方法限制了我们可以适合神经数据的这些模型的数量。在这里，我们提出了一个在决策任务中建模神经活动的统一框架。该框架包括规范漂移扩散模型，并启用扩展，例如多维蓄能器，可变和崩溃的边界以及离散跳跃。我们的框架基于约束复发状态空间模型的参数，为此我们引入了可扩展的变分拉普拉斯 - EM推理算法。在两项决策任务中，我们将建模方法应用于猴子顶皮层记录的尖峰响应。我们发现，二维蓄能器比单个蓄能器模型更好地捕获了一组顶点神经元的试验平均反应。接下来，我们在随机点运动任务中识别了唇神经元反应中的可变下边界。
An open question in systems and computational neuroscience is how neural circuits accumulate evidence towards a decision. Fitting models of decision-making theory to neural activity helps answer this question, but current approaches limit the number of these models that we can fit to neural data. Here we propose a unifying framework for modeling neural activity during decision-making tasks. The framework includes the canonical drift-diffusion model and enables extensions such as multi-dimensional accumulators, variable and collapsing boundaries, and discrete jumps. Our framework is based on constraining the parameters of recurrent state-space models, for which we introduce a scalable variational Laplace-EM inference algorithm. We applied the modeling approach to spiking responses recorded from monkey parietal cortex during two decision-making tasks. We found that a two-dimensional accumulator better captured the trial-averaged responses of a set of parietal neurons than a single accumulator model. Next, we identified a variable lower boundary in the responses of an LIP neuron during a random dot motion task.
