A recurrent network architecture explains tectal activity dynamics and experience-dependent behaviour
A recurrent network architecture explains tectal activity dynamics and experience-dependent behaviour
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DOI：
10.1101/2022.03.30.486335
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发表时间：
2022-04
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影响因子：
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通讯作者：
Asaph Zylbertal;I. H. Bianco
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作者：
Asaph Zylbertal;I. H. Bianco
神经元群体的持续活动代表了一种内部大脑状态，它影响着感官信息如何被处理以控制行为。相反，外部感觉输入扰乱网络动态，导致持续超过刺激持续时间的持久效应。然而，这些动态和电路结构之间的关系及其对感觉处理，认知和行为的影响知之甚少。通过将细胞分辨率钙成像与机械网络建模相结合，我们旨在推断斑马鱼视顶盖中的空间和时间网络相互作用，这些相互作用塑造了其对视觉输入的持续活动和状态依赖性反应。我们发现，一个简单的经常性网络架构，其中顶盖动力学占主导地位的快速，短距离，长期的，活动依赖性抑制对抗兴奋，足以解释多个方面的人口活动，包括间歇性爆发，审判到审判的感觉反应变异性和空间选择性反应适应。此外，这些动态还预测了行为趋势，如视觉诱发的捕食反应的选择性习惯化。总的来说，我们证明了一个机械电路模型，建立在一个统一的经常性的连接基序，可以估计一个动态神经网络的偶然状态，并占经验依赖的影响感官编码和视觉引导的行为。
The ongoing activity of neuronal populations represents an internal brain state that influences how sensory information is processed to control behaviour. Conversely, external sensory inputs perturb network dynamics, resulting in lasting effects that persist beyond the duration of the stimulus. However, the relationship between these dynamics and circuit architecture and their impact on sensory processing, cognition and behaviour are poorly understood. By combining cellular-resolution calcium imaging with mechanistic network modelling, we aimed to infer the spatial and temporal network interactions in the zebrafish optic tectum that shape its ongoing activity and state-dependent responses to visual input. We showed that a simple recurrent network architecture, wherein tectal dynamics are dominated by fast, short range, excitation countered by long-lasting, activity-dependent suppression, was sufficient to explain multiple facets of population activity including intermittent bursting, trial-to-trial sensory response variability and spatially-selective response adaptation. Moreover, these dynamics also predicted behavioural trends such as selective habituation of visually evoked prey-catching responses. Overall, we demonstrate that a mechanistic circuit model, built upon a uniform recurrent connectivity motif, can estimate the incidental state of a dynamic neural network and account for experience-dependent effects on sensory encoding and visually guided behaviour.