Single Spike Models of Predictive Coding

描述：(申请人提供) 感觉神经元对刺激的编码通常被视为特征检测 模板匹配。在这些模型中，每个神经元对其首选输入做出反应 具有最高射速的模式。这一观点有几个 缺点：1)刺激专一性--每个神经元最佳地只编码一个 刺激。2)反应模棱两可--各种非最佳刺激引起的反应相同 回应。3)行为意义--与任务相关和无关的刺激包括 一样的。 我们最近提出了一种时空结构与 种群编码有可能克服 特征匹配/码率编码方法。我们假设刺激的属性 由大脑皮层分布式网络的放电模式表示 神经元。这样的网络被称为预测编码网络，可以广泛地 使用反馈从输入的统计中学习他们的接受域 刺激物。 我们建议在猕猴背部神经元的研究中检验这一假说。 纹状外视皮质。MT区和MST区的视觉运动处理 为测试相关的神经编码模型提供理想的设置 刺激是复杂的、时变的，用于自然主义行为。我们 我将首先开发MST对局部运动响应的预测编码模型 刺激和全场光流。然后我们将通过确定以下内容来测试该模型 MST反馈对MT神经元反应的预测效应是否 与预测编码模型一致。接下来，我们将衡量人们的反应 视觉刺激与自我运动和追逐相结合时MST细胞的变化 目标。最后，我们将让猴子参与与刺激相关的行为任务 确定人口分布的同步性是否可以识别刺激 任务效应在MT和MST反应中的作用。MT-MST响应被很好地描述 在一篇无法解释接受野的大量文献中 MST的机制或更高阶视运动反应。我们的协作 MT-MST尖峰列车详细模型的开发将直接测试我们的 建立并阐明运动知觉的大脑皮层机制。

DESCRIPTION:(provided by the applicant) Stimulus encoding by sensory neurons is often viewed as feature detection by template matching. In these models, each neuron responds to its preferred input pattern with its highest firing rate. This perspective has several disadvantages: 1) Stimulus specificity-each neuron optimally encodes only one stimulus. 2) Response ambiguity-various non-optimal stimuli evoke identical responses. 3) Behavioral significance- task-linked and irrelevant stimuli are the same. We have recently proposed a link between spatio-temporal structure and population encoding that has the prospect of overcoming the difficulties of the feature matching/rate coding approach. We hypothesized that stimulus attributes are represented by the firing patterns of distributed networks of cortical neurons. Such networks, termed predictive coding networks, can make extensive use of feedback to learn their receptive fields from the statistics of input stimuli. We propose to test this hypothesis in studies of neurons in macaque dorsal extrastriate visual cortex. Visual motion processing in areas MT and MST provide an ideal setting for testing models of neural coding as the relevant stimuli are complex, time varying, and are used in naturalistic behaviors. We will first develop a predictive coding model of MST responses to local motion stimuli and full-field optic flow. We will then test that model by determining whether the predicted effects of MST feedback on MT neuronal responses are consistent with the predictive coding model. Next we will measure the responses of MST cells when visual stimuli are combined with self-movement and pursuit targets. Finally, we will engage the monkey in stimulus linked behavioral tasks to determine whether population-distributed synchrony might identify stimulus and task effects in MT and MST responses. MT-MST responses are well-described in an extensive literature that has failed to explain the receptive field mechanisms or higher-order visual motion responses in MST. Our collaborative development of a detailed model of MT-MST spike trains will directly test our model and elucidate the cortical mechanisms of motion perception.