Single Spike Models of Predictive Coding

描述：（申请人提供） 感觉神经元的刺激编码通常被视为特征检测， 模板匹配在这些模型中，每个神经元都响应其首选输入 以最高的发射率。这个观点有几个 缺点：1）刺激特异性-每个神经元最佳编码只有一个 刺激。2)反应模糊-各种非最佳刺激引起相同的 应答3)行为意义-任务相关和无关的刺激是 一样的 我们最近提出了时空结构和 人口编码，有克服困难的前景， 特征匹配/速率编码方法。我们假设刺激属性 是由大脑皮层的分布式网络的放电模式所代表的 神经元这样的网络，称为预测编码网络，可以使广泛的 使用反馈从输入的统计数据中了解他们的接受域 刺激。 我们建议在猕猴背侧神经元的研究中验证这一假设 纹外视皮层MT和MST区的视觉运动加工 为测试神经编码模型提供了理想的设置， 刺激是复杂的、随时间变化的，并且用于自然主义行为。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 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，我们将测量 当视觉刺激与自我运动和追逐相结合时， 目标的最后，我们将让猴子参与刺激相关的行为任务， 以确定群体分布的同步性是否可以识别刺激 MT和MST反应的任务效应。MT-MST反应已得到充分描述 在大量的文献中， 机制或高阶视觉运动反应在MST。我们的协作 MT-MST穗列车的详细模型的开发将直接测试我们的 模型和阐明运动知觉的皮层机制。

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.