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首页> 外文期刊>Journal of vision >Combining form and motion - an integrated approach for learning biological motion representations
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Combining form and motion - an integrated approach for learning biological motion representations

机译:结合形式和运动 - 学习生物运动表示的综合方法

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Recognition of biological motion in primates appears to be effortless even in the case of impoverished input, such as point-light stimuli (PLS; no form; Johansson, Perc. & Psych., 1971) or static implied motion stimuli (no motion; Kourtzi & Kanwisher, J. Cogn. Neurosci., 2000). Recent investigations (Peuskens et al. Eur. J. Neurosci., 2005; Giese & Poggio, Nat. Rev. Neurosci., 2003) support the notion that biological motion is processed in parallel, largely independent form and motion pathways, which are integrated at the intermediate cortical level of STS. None of these models yet successfully explains how missing information in one processing channel can be substituted by the complementary channel and enhance neural activation. We aim at proposing a single integrated model that consists of parallel form and motion processing pathways but incorporates an activity transfer between them. Prototypical form and motion pattern representations (IT/MST) are established using a competitive Hebbian learning scheme. An automatic selection of articulated postures is enabled through motion-form interaction during learning. Convergent temporally correlated input to sequence-selective cells in STS is learned by combined bottom-up and top-down learning (Layher et al., LNCS 7552, 2012). Top-down weights strengthen feedback prediction signals which allow STS neurons to prime afferent cells by expected spatio-temporal signatures. Simulation results obtained using the same previously learned representations are shown for both, implied motion and PLS. Responses of form prototypes to static articulated images drive STS cells which, in turn, send feedback signals to corresponding motion prototype representations, giving a possible explanation for increased fMRI responses in human MT+ to implied motion displays. Likewise, motion pattern prototypes probed with PLS reach activation levels comparable to fully textured animated motion sequences. Here, feedback from STS enhances the activities of corresponding prototype cells in area IT, possibly explaining the activation of form templates during PLS presentation (Lange & Lappe, J. Neurosci., 2006).
机译:即使在贫困输入的情况下,识别最初的灵长类动物的生物运动似乎是毫不费力的,例如点光刺激(PLS;没有形式; Johansson,Perc。,1971)或静态隐含运动刺激(没有动作; Kourtzi &Kanwisher,J. Cogn.neurosci。,2000)。最近的调查(Peuskens等。欧元。J. Neurosci。,2005年; Giese&Poggio,NAT。Rev. Neurosci。,2003)支持在集成的平行,主要是独立的形式和运动途径上加工生物运动的观念在STS的中间皮质水平。这些模型中没有一个尚未解释一个处理信道中的缺失信息可以被互补信道代替并增强神经激活。我们的目标是提出一个集成模型,包括并行形式和运动处理路径,而是包含它们之间的活动转移。使用竞争性Hebbian学习计划建立原型形式和运动模式表示(IT / MST)。通过学习期间的运动形式相互作用使得铰接姿势自动选择。通过组合自下而上和自上而下学习来学习STS中的序列选择细胞的收敛时间相关输入(Layher等,LNC 7552,2012)。自上而下的重量加强反馈预测信号,其通过预期的时空签名使STS神经元允许STS神经元。使用相同的先前学习的表示获得的仿真结果显示为两者,默示的运动和PLS。形式原型对静态铰接图像驱动STS电池的响应,其又向相应的运动原型表示发送反馈信号,给出了对Luman MT +中的FMRI响应增加的可能解释。同样地,用PLS探测的运动模式原型达到与完全纹理的动画运动序列相当的激活水平。在这里,STS的反馈增强了区域中的相应原型单元的活动,可能在PLS呈现期间解释了表单模板的激活(Lange&Lappe,J. Neurosci,2006)。

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