Stimuli Reduce the Dimensionality of Cortical Activity.
Stimuli Reduce the Dimensionality of Cortical Activity.
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DOI：
10.3389/fnsys.2016.00011
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发表时间：
2016
影响因子：
3
通讯作者：
La Camera G
中科院分区：
文献类型：
--
作者：
Mazzucato L;Fontanini A;La Camera G
同时记录的神经元集合的活动可以表示为放电率空间中的一组点。即使这个空间的维度等于集合的大小，神经活动也可以有效地定位在较小的子空间上。神经空间的维数是神经活动所支持的计算任务的重要决定因素。在这里，我们调查的维度从警觉大鼠的感觉皮层神经合奏期间正在进行的（试验间）和刺激诱发的活动。我们发现，维数与合奏大小呈线性增长，并在正在进行的活动相比，诱发活动的增长速度显着更快。我们解释这些结果使用的尖峰网络模型的基础上，集群架构。该模型捕捉正在进行的活动和诱发活动之间的增长率的差异，并预测与合奏大小，可以在高密度多电极记录进行测试的特征缩放。此外，我们提出了一个简单的理论，预测存在一个上限的维度。这个上限与成对相关性的数量成反比，并且与没有集群的同质网络相比，它大了一个等于集群数量的因子。这种界限的经验估计取决于试验的次数和持续时间，并很好地预测了理论。总之，这些结果提供了一个框架来分析神经维度在警觉的动物，其行为下的刺激呈现，其理论依赖于合奏的大小，集群的数量，和相关性的尖峰网络模型。
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.