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中文摘要
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理论框架:视觉和触觉共享一个关键功能--对3D物体的感知 形状我们同时交替使用视觉和触觉来识别、理解和 与真实的世界物体进行物理交互,基于对其3D形状的详细理解, 机械功能。方法,创新,物种:我们提出了一个新的探索, 基于3D对象感知的视觉/触觉电路交互:(i)在 单个神经元和动作电位,(ii)在局部神经网络的范围内研究, 线性阵列记录在猴子IT,和(iii)在行为背景下的积极把握,以区别 看不见的物体目的1假设测试:触觉3D物体辨别引起不同的, 形状特异性神经群体反应模式在前猴IT。这一预测结果将 提供了强有力的证据,证明视觉对象通路的最后阶段也具有触觉起源 信息足以通过触摸来区分3D形状,正如人类fMRI所建议的那样。 目的2:假设检验:猴的触觉形状依赖性反应体现了一种 用于3D形状的组合几何代码,类似于用于视觉3D形状的IT代码。我们有 先前表明,IT神经元对3D对象刺激的响应编码体积形状片段, 高维几何空间这些信号提供了IT集成的基本要素 神经元将对象表示为3D空间组合。我们预测类似的成分编码 对于触觉3D形状,显示视觉和触觉汇聚在同一个强大的编码解决方案上。 目的3假设检验:单个IT神经元携带相同的3D形状信息, 触觉和视觉对象。这个预测结果一致的视觉和触觉编码由相同的 神经元将证明前IT携带统一的超模态3D对象表示。 实现未来BCP R 01,长期目标:任何一个目标的成功都将证明 随后的TargetBCP R 01应用程序,用于表征触觉和视觉电路如何相互作用,以: (i)在连接多个处理级(视觉区域V1,V2, V4、IT后部、IT前部;躯体感觉区SI、SII、上级顶骨、顶骨)。(ii)动态 联合收割机并将视觉和触觉电路上的3D形状信息细化为一致的、冗余的或 从这两种模态积累了冲突的信息。(iii)在新对象学习中进行交互,因为 这两种模式几乎肯定会提供相互监督,特别是在发展期间, 视觉必须校准如此多的间接线索,以获得可直接用于触摸的3D形状信息。 长期目标是了解视觉和触觉皮层回路如何完善,协调, 合成两种非常不同感觉输入,以产生对3D形状的统一认知欣赏。
英文摘要
THEORETICAL FRAMEWORK: Vision and touch share a critical function—perception of 3D object shape. We use vision and touch both alternatively and simultaneously to recognize, understand, and interact physically with real world objects, based on detailed apprehension of their 3D shapes and mechanical functionality. METHODS, INNOVATION, SPECIES: We propose a novel exploration of visual/haptic circuit interactions underlying 3D object perception: (i) at the spatiotemporal resolution of individual neurons and action potentials, (ii) across the scope of local neural networks studied with linear array recording in monkey IT, and (iii) in the behavioral context of active grasp to discriminate unseen objects. AIM 1 TEST OF HYPOTHESIS: Haptic 3D object discrimination evokes distinct, shape-specific neural population response patterns in anterior monkey IT. This predicted result would provide strong evidence that the final stage in the visual object pathway also carries haptic-origin information sufficient to discriminate 3D shapes through touch, as suggested by human fMRI. AIM 2 TEST OF HYPOTHESIS: Haptic shape-dependent responses in monkey IT embody a compositional geometric code for 3D shape, analogous to the IT code for visual 3D shape. We have previously shown that IT neurons responding to 3D object stimuli encode volumetric shape fragments in a high-dimensional geometric space. These signals provide the essential elements for ensembles of IT neurons to represent objects as 3D spatial compositions. We predict analogous compositional coding for haptic 3D shape, showing that vision and touch converge on the same powerful coding solution. AIM 3 TEST OF HYPOTHESIS: Individual IT neurons carry identical 3D shape information about haptic and visual objects. This predicted result of congruent visual and haptic coding by the same neurons would demonstrate that anterior IT carries a unified, supramodal 3D object representation. ENABLING A FUTURE BCP R01, LONG-TERM GOAL: Success on any of these aims would justify a subsequent TargetedBCP R01 application to characterize HOW haptic and visual circuits interact to: (i) exchange information across circuits connecting multiple processing stages (visual areas V1, V2, V4, posterior IT, anterior IT; somatosensory areas SI, SII, superior parietal, insula). (ii) dynamically combine and refine 3D shape information across visual and haptic circuits as congruent, redundant, or conflicting information accumulates from the two modalities. (iii) interact in new object learning, since the two modalities almost certainly provide mutual supervision, especially during development, when vision must calibrate so many indirect cues for 3D shape information directly available to touch. The LONGTERM GOAL is to understand how visual and haptic cortical circuits refine, reconcile, and synthesize two very different sensory inputs to produce a unified cognitive appreciation of 3D shape.
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Early representation of 3D volumetric shape in visual object processing
  • 批准号:
    10412966
  • 项目类别:
  • 资助金额:
    $48.43万
  • 财政年份:
    2018
  • 负责人:
    CHARLES E CONNOR
  • 依托单位:
Shape Learning: Computational Changes in Chronically Studied Neural Populations
  • 批准号:
    8858962
  • 项目类别:
  • 资助金额:
    $31.99万
  • 财政年份:
    2015
  • 负责人:
    CHARLES E CONNOR
  • 依托单位:
Shape Learning: Computational Changes in Chronically Studied Neural Populations
  • 批准号:
    9248364
  • 项目类别:
  • 资助金额:
    $42.46万
  • 财政年份:
    2015
  • 负责人:
    CHARLES E CONNOR
  • 依托单位:
Sensory Feedback for upper limb neuroprosthetics
  • 批准号:
    8671867
  • 项目类别:
  • 资助金额:
    $35.44万
  • 财政年份:
    2014
  • 负责人:
    CHARLES E CONNOR
  • 依托单位:
海外基金