Shlens Jonathon, Field Greg D, Gauthier Jeffrey L, Grivich Matthew I, Petrusca Dumitru, Sher Alexander, Litke Alan M, Chichilnisky E J
Department of Systems Neurobiology, The Salk Institute, La Jolla, California 92037, USA.
J Neurosci. 2006 Aug 9;26(32):8254-66. doi: 10.1523/JNEUROSCI.1282-06.2006.
Current understanding of many neural circuits is limited by our ability to explore the vast number of potential interactions between different cells. We present a new approach that dramatically reduces the complexity of this problem. Large-scale multi-electrode recordings were used to measure electrical activity in nearly complete, regularly spaced mosaics of several hundred ON and OFF parasol retinal ganglion cells in macaque monkey retina. Parasol cells exhibited substantial pairwise correlations, as has been observed in other species, indicating functional connectivity. However, pairwise measurements alone are insufficient to determine the prevalence of multi-neuron firing patterns, which would be predicted from widely diverging common inputs and have been hypothesized to convey distinct visual messages to the brain. The number of possible multi-neuron firing patterns is far too large to study exhaustively, but this problem may be circumvented if two simple rules of connectivity can be established: (1) multi-cell firing patterns arise from multiple pairwise interactions, and (2) interactions are limited to adjacent cells in the mosaic. Using maximum entropy methods from statistical mechanics, we show that pairwise and adjacent interactions accurately accounted for the structure and prevalence of multi-neuron firing patterns, explaining approximately 98% of the departures from statistical independence in parasol cells and approximately 99% of the departures that were reproducible in repeated measurements. This approach provides a way to define limits on the complexity of network interactions and thus may be relevant for probing the function of many neural circuits.
目前，我们对许多神经回路的理解受到自身能力的限制，即难以探究不同细胞之间大量潜在的相互作用。我们提出了一种新方法，能大幅降低这一问题的复杂性。利用大规模多电极记录技术，我们对猕猴视网膜中数百个ON型和OFF型伞状视网膜神经节细胞几乎完整且规则排列的镶嵌结构中的电活动进行了测量。正如在其他物种中观察到的那样，伞状细胞表现出显著的成对相关性，这表明存在功能连接。然而，仅通过成对测量不足以确定多神经元放电模式的普遍性，这种模式可由广泛分散的共同输入预测得出，并且据推测会向大脑传递不同的视觉信息。可能的多神经元放电模式数量太多，无法进行详尽研究，但如果能建立两条简单的连接规则，这个问题或许可以得到解决：（1）多细胞放电模式源自多个成对相互作用；（2）相互作用仅限于镶嵌结构中的相邻细胞。我们运用统计力学中的最大熵方法表明，成对和相邻相互作用准确地解释了多神经元放电模式的结构和普遍性，解释了伞状细胞中约98%偏离统计独立性的情况，以及在重复测量中约99%可重现的偏离情况。这种方法提供了一种定义网络相互作用复杂性限制的途径，因此可能与探究许多神经回路的功能相关。
