Recurrent network interactions explain tectal response variability and experience-dependent behavior.
Recurrent network interactions explain tectal response variability and experience-dependent behavior.
复制标题
经常性网络相互作用解释了直肠响应的变异性和经验依赖性行为。
DOI：
10.7554/elife.78381
复制
发表时间：
2023-03-21
期刊：
影响因子：
7.7
通讯作者：
Bianco IH
中科院分区：
文献类型：
--
作者：
Zylbertal A;Bianco IH
反应变异性是感觉加工和行为的基本和普遍特征。它源于大脑内部状态的波动，这种波动调节着感觉信息的表现和转化，以指导行为行为。在某种程度上，大脑状态是由最近的网络活动形成的，通过循环连接来调节神经元的兴奋性。然而，人们对这些相互作用对响应变异性的影响程度及其作用的时空尺度了解甚少。在这里，我们将人口记录和建模相结合，以深入了解神经元活动如何调节网络状态，从而影响视觉诱发的活动和行为。首先，我们对视神经顶盖进行了细胞分辨率钙成像，以监测正在进行的活动，其模式既是网络状态变化的原因也是结果。我们开发了一个最小的网络模型，结合了快速、短距离、反复激发和持久的、活动依赖的抑制，再现了顶盖活动的标志性特征——间歇性破裂。接下来，我们根据最近的活动历史使用该模型来估计顶盖神经元的兴奋状态，并发现这解释了部分视觉诱发反应的试验到试验的可变性，以及空间选择性反应适应。此外，这些动态还预测了视觉诱发的猎物捕获的选择性习惯等行为趋势。总的来说，我们证明了一个简单的循环交互基序可以用来估计活动对神经网络附带状态的影响，并解释对感觉编码和视觉引导行为的经验依赖效应。
Response variability is an essential and universal feature of sensory processing and behavior. It arises from fluctuations in the internal state of the brain, which modulate how sensory information is represented and transformed to guide behavioral actions. In part, brain state is shaped by recent network activity, fed back through recurrent connections to modulate neuronal excitability. However, the degree to which these interactions influence response variability and the spatial and temporal scales across which they operate, are poorly understood. Here, we combined population recordings and modeling to gain insights into how neuronal activity modulates network state and thereby impacts visually evoked activity and behavior. First, we performed cellular-resolution calcium imaging of the optic tectum to monitor ongoing activity, the pattern of which is both a cause and consequence of changes in network state. We developed a minimal network model incorporating fast, short range, recurrent excitation and long-lasting, activity-dependent suppression that reproduced a hallmark property of tectal activity – intermittent bursting. We next used the model to estimate the excitability state of tectal neurons based on recent activity history and found that this explained a portion of the trial-to-trial variability in visually evoked responses, as well as spatially selective response adaptation. Moreover, these dynamics also predicted behavioral trends such as selective habituation of visually evoked prey-catching. Overall, we demonstrate that a simple recurrent interaction motif can be used to estimate the effect of activity upon the incidental state of a neural network and account for experience-dependent effects on sensory encoding and visually guided behavior.