Louie Kenway, LoFaro Thomas, Webb Ryan, Glimcher Paul W
Center for Neural Science, and Institute for the Interdisciplinary Study of Decision Making, New York University, New York, New York 10003,
Department of Mathematics and Computer Science, Gustavus Adolphus College, St Peter, Minnesota 56082, and.
J Neurosci. 2014 Nov 26;34(48):16046-57. doi: 10.1523/JNEUROSCI.2851-14.2014.
Normalization is a widespread neural computation, mediating divisive gain control in sensory processing and implementing a context-dependent value code in decision-related frontal and parietal cortices. Although decision-making is a dynamic process with complex temporal characteristics, most models of normalization are time-independent and little is known about the dynamic interaction of normalization and choice. Here, we show that a simple differential equation model of normalization explains the characteristic phasic-sustained pattern of cortical decision activity and predicts specific normalization dynamics: value coding during initial transients, time-varying value modulation, and delayed onset of contextual information. Empirically, we observe these predicted dynamics in saccade-related neurons in monkey lateral intraparietal cortex. Furthermore, such models naturally incorporate a time-weighted average of past activity, implementing an intrinsic reference-dependence in value coding. These results suggest that a single network mechanism can explain both transient and sustained decision activity, emphasizing the importance of a dynamic view of normalization in neural coding.
归一化是一种广泛存在的神经计算方式，在感觉处理中介导除法增益控制，并在与决策相关的额叶和顶叶皮层中实现依赖于上下文的价值编码。尽管决策是一个具有复杂时间特征的动态过程，但大多数归一化模型都是与时间无关的，对于归一化与选择之间的动态相互作用知之甚少。在这里，我们表明一个简单的归一化微分方程模型可以解释皮层决策活动的特征性相位-持续模式，并预测特定的归一化动态：初始瞬态期间的价值编码、随时间变化的价值调制以及上下文信息的延迟出现。从实验上看，我们在猴子外侧顶内皮层与扫视相关的神经元中观察到了这些预测的动态。此外，此类模型自然地纳入了过去活动的时间加权平均值，在价值编码中实现了内在的参考依赖性。这些结果表明，单一的网络机制可以解释瞬态和持续的决策活动，强调了在神经编码中对归一化进行动态观察的重要性。
