NEURONAL MODELING. Single-trial spike trains in parietal cortex reveal discrete steps during decision-making.
NEURONAL MODELING. Single-trial spike trains in parietal cortex reveal discrete steps during decision-making.
复制标题
神经元建模。在决策过程中，在顶叶皮层中的单次尖峰火车揭示了离散的步骤。
DOI：
10.1126/science.aaa4056
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发表时间：
2015-07-10
期刊：
影响因子：
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通讯作者：
Pillow JW
中科院分区：
文献类型：
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作者：
Latimer KW;Yates JL;Meister ML;Huk AC;Pillow JW
猕猴外侧顶内（LIP）区域的神经元在决策过程中表现出向上或向下倾斜的放电率。这些斜坡通常被认为反映了证据朝着决策阈值的逐渐积累。然而，试验平均响应的斜坡可能是由不同试验中不同时间的瞬时跳跃引起的。我们研究了单次试验响应LIP使用统计方法拟合和比较潜在的动态尖峰序列模型。我们比较了模型与潜在的尖峰率由连续扩散到约束动力学或离散“步进”动力学。我们记录的选择神经元中，大约有四分之三可以用步进模型更好地描述。此外，推断出的步骤比尖峰计数携带了更多关于动物选择的信息。
Neurons in the macaque lateral intraparietal (LIP) area exhibit firing rates that appear to ramp upwards or downwards during decision-making. These ramps are commonly assumed to reflect the gradual accumulation of evidence towards a decision threshold. However, the ramping in trial-averaged responses could instead arise from instantaneous jumps at different times on different trials. We examined single-trial responses in LIP using statistical methods for fitting and comparing latent dynamical spike train models. We compared models with latent spike rates governed by either continuous diffusion-to-bound dynamics or discrete “stepping” dynamics. Roughly three-quarters of the choice-selective neurons we recorded were better described by the stepping model. Moreover, the inferred steps carried more information about the animal’s choice than spike counts.