Clustering of Neural Activity: A Design Principle for Population Codes
Clustering of Neural Activity: A Design Principle for Population Codes
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DOI：
10.3389/fncom.2020.00020
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发表时间：
2020-03-13
影响因子：
3.2
通讯作者：
Tkacik, Gasper
中科院分区：
文献类型：
--
作者：
Berry, Michael J., II;Tkacik, Gasper
我们建议，神经元之间的相关性一般足够强，组织成一个离散的集群，每个集群可以被视为一个人口码字的神经活动模式。我们的推理开始于使用最大熵模型对视网膜神经节细胞数据的分析，表明该群体处于沮丧的、边缘亚临界的或玻璃状的状态。这导致了一个论点，即许多其他大脑区域的神经群可能共享这种结构。接下来，我们使用潜变量模型来表明这种玻璃态具有明确定义的神经活动簇。簇具有三个吸引人的性质：（i）簇表现出纠错，即，它们可重复地由相同的刺激引起，尽管在组成神经元的水平上具有可变性;（ii）簇编码与其组成神经元相比在质量上不同的视觉特征;以及（iii）簇可以由下游神经回路以无监督的方式学习。我们假设，这些属性产生了一个“可学习的”神经代码，皮层层次结构使用它来提取越来越复杂的功能，而无需监督或强化。
We propose that correlations among neurons are generically strong enough to organize neural activity patterns into a discrete set of clusters, which can each be viewed as a population codeword. Our reasoning starts with the analysis of retinal ganglion cell data using maximum entropy models, showing that the population is robustly in a frustrated, marginally sub-critical, or glassy, state. This leads to an argument that neural populations in many other brain areas might share this structure. Next, we use latent variable models to show that this glassy state possesses well-defined clusters of neural activity. Clusters have three appealing properties: (i) clusters exhibit error correction, i.e., they are reproducibly elicited by the same stimulus despite variability at the level of constituent neurons; (ii) clusters encode qualitatively different visual features than their constituent neurons; and (iii) clusters can be learned by downstream neural circuits in an unsupervised fashion. We hypothesize that these properties give rise to a "learnable" neural code which the cortical hierarchy uses to extract increasingly complex features without supervision or reinforcement.