Max Planck Institute for Biological Intelligence, Martinsried, Germany.
Ludwig Maximilian University of Munich, Munich, Germany.
Nat Neurosci. 2023 Nov;26(11):1894-1905. doi: 10.1038/s41593-023-01443-z. Epub 2023 Oct 2.
Inhibitory interactions between opponent neuronal pathways constitute a common circuit motif across brain areas and species. However, in most cases, synaptic wiring and biophysical, cellular and network mechanisms generating opponency are unknown. Here, we combine optogenetics, voltage and calcium imaging, connectomics, electrophysiology and modeling to reveal multilevel opponent inhibition in the fly visual system. We uncover a circuit architecture in which a single cell type implements direction-selective, motion-opponent inhibition at all three network levels. This inhibition, mediated by GluClα receptors, is balanced with excitation in strength, despite tenfold fewer synapses. The different opponent network levels constitute a nested, hierarchical structure operating at increasing spatiotemporal scales. Electrophysiology and modeling suggest that distributing this computation over consecutive network levels counteracts a reduction in gain, which would result from integrating large opposing conductances at a single instance. We propose that this neural architecture provides resilience to noise while enabling high selectivity for relevant sensory information.
拮抗神经元通路之间的抑制性相互作用构成了跨脑区和物种的常见回路基元。然而,在大多数情况下,产生拮抗作用的突触连接和生物物理、细胞和网络机制尚不清楚。在这里,我们结合光遗传学、电压和钙成像、连接组学、电生理学和建模来揭示果蝇视觉系统中的多层次拮抗抑制。我们揭示了一种电路结构,其中单个细胞类型在所有三个网络水平上实现了方向选择性、运动拮抗抑制。这种抑制由 GluClα 受体介导,尽管突触数量少了十倍,但在强度上与兴奋相平衡。不同的拮抗网络水平构成了一个嵌套的层次结构,在不断增加的时空尺度上运作。电生理学和建模表明,将这种计算分布在连续的网络水平上可以抵消由于在单个实例中整合大的相反电导而导致的增益降低。我们提出,这种神经结构在提高对相关感觉信息的选择性的同时,为噪声提供了弹性。