Mazzucato Luca, Fontanini Alfredo, La Camera Giancarlo
Department of Neurobiology and Behavior, State University of New York at Stony Brook Stony Brook, NY, USA.
Department of Neurobiology and Behavior, State University of New York at Stony BrookStony Brook, NY, USA; Graduate Program in Neuroscience, State University of New York at Stony BrookStony Brook, NY, USA.
Front Syst Neurosci. 2016 Feb 17;10:11. doi: 10.3389/fnsys.2016.00011. eCollection 2016.
The activity of ensembles of simultaneously recorded neurons can be represented as a set of points in the space of firing rates. Even though the dimension of this space is equal to the ensemble size, neural activity can be effectively localized on smaller subspaces. The dimensionality of the neural space is an important determinant of the computational tasks supported by the neural activity. Here, we investigate the dimensionality of neural ensembles from the sensory cortex of alert rats during periods of ongoing (inter-trial) and stimulus-evoked activity. We find that dimensionality grows linearly with ensemble size, and grows significantly faster during ongoing activity compared to evoked activity. We explain these results using a spiking network model based on a clustered architecture. The model captures the difference in growth rate between ongoing and evoked activity and predicts a characteristic scaling with ensemble size that could be tested in high-density multi-electrode recordings. Moreover, we present a simple theory that predicts the existence of an upper bound on dimensionality. This upper bound is inversely proportional to the amount of pair-wise correlations and, compared to a homogeneous network without clusters, it is larger by a factor equal to the number of clusters. The empirical estimation of such bounds depends on the number and duration of trials and is well predicted by the theory. Together, these results provide a framework to analyze neural dimensionality in alert animals, its behavior under stimulus presentation, and its theoretical dependence on ensemble size, number of clusters, and correlations in spiking network models.
同时记录的神经元集群的活动可以表示为发放率空间中的一组点。尽管这个空间的维度等于集群大小，但神经活动可以有效地定位在较小的子空间上。神经空间的维度是神经活动所支持的计算任务的一个重要决定因素。在这里，我们研究清醒大鼠感觉皮层神经集群在持续（试验间期）和刺激诱发活动期间的维度。我们发现维度随着集群大小呈线性增长，并且在持续活动期间比诱发活动增长得明显更快。我们使用基于聚类架构的脉冲网络模型来解释这些结果。该模型捕捉了持续活动和诱发活动之间增长率的差异，并预测了一种与集群大小相关的特征缩放关系，这可以在高密度多电极记录中进行测试。此外，我们提出了一个简单的理论，预测维度存在一个上限。这个上限与成对相关性的量成反比，并且与没有聚类的均匀网络相比，它大一个等于聚类数量的因子。这种界限的经验估计取决于试验的数量和持续时间，并且该理论能很好地预测。总之，这些结果提供了一个框架，用于分析清醒动物的神经维度、其在刺激呈现下的行为以及其在脉冲网络模型中对集群大小、聚类数量和相关性的理论依赖性。
