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Spontaneous Neuronal Network Dynamics Reveal Circuit's Functional Adaptations for Behavior

机译:自发神经网络动力学揭示电路对行为的功能适应

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Spontaneous neuronal activity is spatiotemporally structured, influencing brain computations. Nevertheless, the neuronal interactions underlying these spontaneous activity patterns, and their biological relevance, remain elusive. Here, we addressed these questions using two-photon calcium imaging of intact zebrafish larvae to monitor the neuron-to-neuron spontaneous activity fine structure in the tectum, a region involved in visual spatial detection. Spontaneous activity was organized in topographically compact assemblies, grouping functionally similar neurons rather than merely neighboring ones, reflecting the tectal retinotopic map despite being independent of retinal drive. Assemblies represent all-or-none-like sub-networks shaped by competitive dynamics, mechanisms advantageous for visual detection in noisy natural environments. Notably, assemblies were tuned to the same angular sizes and spatial positions as prey-detection performance in behavioral assays, and their spontaneous activation predicted directional tail movements. Therefore, structured spontaneous activity represents "preferred'' network states, tuned to behaviorally relevant features, emerging from the circuit's intrinsic non-linear dynamics, adapted for its functional role.
机译:自发性神经元活动是时空结构的,影响大脑的计算。然而,这些自发活动模式背后的神经元相互作用及其生物学相关性仍然难以捉摸。在这里,我们使用完整的斑马鱼幼虫的两光子钙成像技术来解决这些问题,以监测盖层中涉及视觉空间检测的区域的神经元至神经元自发活动的精细结构。自发活动以地形紧凑的组件进行组织,将功能相似的神经元而不是相邻的神经元分组,反映了尽管视网膜驱动独立,但其视网膜视听部位图。组件表示由竞争性动力学形成的全非子网络,这种网络有利于在嘈杂的自然环境中进行视觉检测。值得注意的是,程序集已调整为与行为分析中的猎物检测性能相同的角度大小和空间位置,并且它们的自发激活可预测方向性尾巴移动。因此,结构化的自发活动表示“首选”网络状态,该状态已从电路的固有非线性动力学中产生,并根据行为功能进行了调整,以适应行为相关的特征。

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