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Single Spike Models of Predictive Coding

预测编码的单尖峰模型

基本信息

批准号：
6607344
负责人：
DANA H. BALLARD
金额：
$ 31.9万
依托单位：
UNIVERSITY OF ROCHESTER
依托单位国家：
美国
项目类别：
财政年份：
2001
资助国家：
美国
起止时间：
2001-09-14 至 2005-06-30
项目状态：
已结题

项目摘要

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.

描述：（由申请人提供）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，每个神经元都会对其首选输入做出响应具有最高发射率的模式。这个观点有几个缺点：1）刺激特异性——每个神经元最优只编码一个刺激。 2）反应模糊性——各种非最优刺激引起相同的刺激回应。 3) 行为意义——与任务相关的和不相关的刺激相同。我们最近提出了时空结构与人口编码有望克服困难特征匹配/速率编码方法。我们假设刺激属性由皮质分布式网络的放电模式表示神经元。这种网络被称为预测编码网络，可以进行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检验这一假设纹状体外视觉皮层。 MT 和 MST 领域的视觉运动处理为测试神经编码模型提供理想的设置刺激是复杂的、随时间变化的，并且用于自然行为。我们将首先开发 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来测试该模型 MST 反馈对 MT 神经元反应的预测影响是否与预测编码模型一致。接下来我们将测量响应当视觉刺激与自我运动和追踪相结合时，MST细胞的数量目标。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是否可以识别刺激以及 MT 和 MST 响应中的任务效果。 MT-MST 响应已得到很好的描述在大量未能解释感受野的文献中 MST 中的机制或高阶视觉运动反应。我们的合作 MT-MST尖峰列车详细模型的开发将直接测试我们的建模并阐明运动感知的皮质机制。