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首页> 外文会议>Asilomar Conference on Signals, Systems and Computers >Model-based decoding of time-varying visual information during saccadic eye movements using population-level information
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Model-based decoding of time-varying visual information during saccadic eye movements using population-level information

机译：使用人群级别信息在眼周运动时基于模型的时变视觉信息解码

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Our visual system scans the environment by directed ballistic movements of the eyes, known as saccades, to bring the locations of interest to the fovea for further visual processing. Accurately characterizing visual responses in the perisaccadic period is an important step toward understanding how the visual world is represented during saccades, and therefore how the brain maintains the stability of visual perception across saccades. Here we use our recently developed probabilistic model in the generalized linear model framework, which uses both simultaneously recorded spiking responses and the local field potential, to extract time-varying visual information represented by individual neurons in the middle temporal cortex during saccades. Our results indicate that the optimal model-based decoding exploiting the response correlation structure between neurons, or the network state of the neuronal population, or both, extracts significantly more information about the visual scene than decoding based solely on the single neuron spiking activity. This model-based approach reveals the role of several external and internal covariates in the extrastriate coding of visual stimulus during eye movements. Moreover, the modelbased decoding provides a general framework for testing the contribution of specific perisaccadic changes in neuronal responses to our perception of the visual scene during eye movements.
机译：我们的视觉系统通过定向的眼球弹道运动（称为扫视）扫描环境，将感兴趣的位置带到中央凹处，以进行进一步的视觉处理。在眼周期间准确表征视觉反应，是朝着了解扫视过程中视觉世界如何表现以及大脑如何在整个扫视过程中保持视觉感知的稳定性迈出的重要一步。在这里，我们在广义线性模型框架中使用我们最近开发的概率模型，该模型同时使用同时记录的尖峰响应和局部场电势，来提取扫视期间中颞叶皮层中单个神经元代表的时变视觉信息。我们的结果表明，与仅基于单个神经元尖峰活动的解码相比，利用神经元之间或神经元群体的网络状态或两者兼有的网络状态之间的响应相关结构的基于最佳模型的解码提取的视觉场景信息要多得多。这种基于模型的方法揭示了在眼球运动过程中，视觉刺激的外部编码中几个外部和内部协变量的作用。此外，基于模型的解码提供了一个通用框架，用于测试眼球运动期间神经元反应中特定眼周变化对我们对视觉场景感知的贡献。

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