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首页> 外文期刊>Neuron >Discrete Stepping and Nonlinear Ramping Dynamics Underlie Spiking Responses of LIP Neurons during Decision-Making
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Discrete Stepping and Nonlinear Ramping Dynamics Underlie Spiking Responses of LIP Neurons during Decision-Making

机译：离散的踩踏和非线性斜坡动力学在决策期间唇神经元的尖峰反应

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摘要

Neurons in LIP exhibit ramping trial-averaged responses during decision-making. Recent work sparked debate over whether single-trial LIP spike trains are better described by discrete "stepping" or continuous "ramping" dynamics. We extended latent dynamical spike train models and used Bayesian model comparison to address this controversy. First, we incorporated non-Poisson spiking into both models and found that more neurons were better described by stepping than ramping, even when conditioned on evidence or choice. Second, we extended the ramping model to include a non-zero baseline and compressive output nonlinearity. This model accounted for roughly as many neurons as the stepping model. However, latent dynamics inferred under this model exhibited high diffusion variance for many neurons, softening the distinction between continuous and discrete dynamics. Results generalized to additional datasets, demonstrating that substantial fractions of neurons are well described by either stepping or nonlinear ramping, which may be less categorically distinct than the original labels implied.
机译：在决策期间，唇部中的神经元表现出斜坡的试用响应。最近的工作引发了单次试用唇峰值列车是否通过离散的“踩平”或连续的“斜坡”动态来更好地描述。我们扩展了潜在动态尖峰列车模型，并使用贝叶斯模型比较来解决这一争议。首先，我们将非泊松尖刺掺入两种型号，发现即使在证据或选择的条件时，踩踏比斜坡更好地描述了更多的神经元。其次，我们扩展了斜坡模型，包括非零基线和压缩输出非线性。该模型占据了许多神经元作为步进模型。然而，在该模型下推断的潜在动力学表现出许多神经元的高扩散差异，软化连续和离散动力学之间的区别。结果一般地推向额外的数据集，证明了通过踩踏或非线性斜坡的大量神经元进行很好地描述，这可能比所暗示的原始标签较低。

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