CRCNS: Computational Models of Multisensory Processing

CRCNS：多感官处理的计算模型

• 批准号: 7615874
• 负责人: M ALEX MEREDITH
• 金额: $ 22.35万
• 依托单位: VIRGINIA COMMONWEALTH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7615874
• 关键词: Address; Animals; Anterior; Architecture; Area; Arts; Auditory; Behavior; Behavioral; Biological; Brain; Complex; Comprehension; Computer Simulation; Computer information processing; Cues; Data; Data Collection; Depth; Detection; Development; Devices; Elements; Exhibits; Fire - disasters; Generations; Horns; Housing; Human; International; Interneurons; Invertebrates; Investigation; Lateral; Lead; Measurement; Measures; Mediating; Medicine; Modality; Modeling; Nature; Nervous system structure; Neurons; Outcome; Perception; Perceptual Disorders; Principal Investigator; Process; Property; Randomized; Range; Research; Robotics; Role; SIV; Sampling; Sensory; Series; Signal Transduction; Source; Speech; Speech Perception; Stimulus; Synaptic plasticity; System; Techniques; Testing; Textiles; Time; Training; Variant; Visual; area V1; base; data modeling; design; extrastriate visual cortex; in vivo; inhibitory neuron; insight; model design; neural circuit; novel; relating to nervous system; repository; research study; response; simulation; somatosensory; superior colliculus Corpora quadrigemina; theories; Address; Animals; Anterior; Architecture; Area; Arts; Auditory; Behavior; Behavioral; Biological; Brain; Complex; Comprehension; Computer Simulation; Computer information processing; Cues; Data; Data Collection; Depth; Detection; Development; Devices; Elements; Exhibits; Fire - disasters; Generations; Horns; Housing; Human; International; Interneurons; Invertebrates; Investigation; Lateral; Lead; Measurement; Measures; Mediating; Medicine; Modality; Modeling; Nature; Nervous system structure; Neurons; Outcome; Perception; Perceptual Disorders; Principal Investigator; Process; Property; Randomized; Range; Research; Robotics; Role; SIV; Sampling; Sensory; Series; Signal Transduction; Source; Speech; Speech Perception; Stimulus; Synaptic plasticity; System; Techniques; Testing; Textiles; Time; Training; Variant; Visual; area V1; base; data modeling; design; extrastriate visual cortex; in vivo; inhibitory neuron; insight; model design; neural circuit; novel; relating to nervous system; repository; research study; response; simulation; somatosensory; superior colliculus Corpora quadrigemina; theories

DESCRIPTION (provided by applicant): Multisensory processing not only contributes to our complex perceptual fabric, but also underlies such disparate effects as speech comprehension as well as detection and orientation behaviors. Consequently, it would be expected that multisensory circuitry would be as varied as its behavioral/perceptual involvements. However, since 1966 (Horn and Hill) there has been only a single model of multisensory convergence and processing: the bimodal neuron. This neuron exhibits suprathreshold excitatory responses to stimuli from more than one modality (e.g., visual alone, somatosensory alone) as well as can produce an integrated response when those stimuli are combined (e.g., visual and somatosensory). Although numerous studies have affirmed the role of bimodal integration (especially in the superior colliculus) in detection and orientation behaviors, it is unclear how these dramatic response changes might influence more subtle levels of activity underlying cortical perceptual functions. Toward that end, recent direct examinations of multisensory processing in cortex have identified a 'new' form of multisensory neuron (Meredith, 2002; Dehner et al., 2004; Allman and Meredith, 2007; reviewed by Driver and Noesselt, 2008). The subthreshold multisensory neuron is excited by stimulation from only one sensory modality, but that activity can be modulated (facilitated or suppressed) by concurrent stimulation in an otherwise ineffective modality. As a consequence, the range of multisensory architectures has expanded beyond that of the traditional bimodal neuron, and even suggests that multisensory convergence may occur along a continuum from bimodal integration at one end, through subthreshold modulation to non-integrative unimodal processing at the other. Because existing computer models of multisensory processing are inadequate to describe these new findings, and because computer models can explore different biological scenarios that are not experimentally accessible, the present series of experiments proposes a computational examination of the spectrum of multisensory processing. First, using 128- channel recording, real in vivo sensory and multisensory data will be obtained from networks of sensory cortical neurons, including unimodal as well as bimodal and subthreshold types. Second, that biological data will be used in a computer simulation that incorporates (a) an integrate and fire spiking neuron model, b) static and dynamic synaptic plasticity rules, (c) hierarchical network topology that includes both excitatory and inhibitory neurons, and (d) introduces randomness to the network connections. Each of these features is novel to models of multisensory processing. Furthermore, real neuronal response data to single-modality stimulation along with data obtained in response to combinations of the same stimuli (derived from in vivo experiments) will be used to design and validate the model. This simulation will be used not only to model the different forms of multisensory processing, but also examine their responses to different levels and sources of inhibition as well as test the numerous variations of the multisensory continuum hypothesis. Once the model is operational, a website will be constructed to allow anyone to submit their own data to test the model. In this way, multisensory data from additional neural areas and species (potentially from invertebrates to humans) will be collected and compared, thereby providing an international repository and clearing-house for multisensory processing. The intellectual merit of the proposed investigations includes not only the development of a complex and sophisticated model of multisensory neuronal and network processing using state-of-the-art computing theories and biological verification, but also will provide insight and testable hypotheses regarding the fundamental properties of multisensory processing itself and its relation to behavior, perception and perceptual disorders. The broad impact of these efforts will contribute to progress toward the ultimate challenge of building artificial systems that perform on par with humans and thus facilitate the development of effective artificial devices for applications in robotics and medicine.

描述（由申请人提供）：多感官处理不仅有助于我们复杂的感知结构，而且还构成了语音理解以及检测和方向行为等不同效果的基础。因此，可以预期，多感官电路将与其行为/感知参与一样多样化。但是，自1966年（Horn和Hill）以来，只有单一的多感官收敛与加工模型：双峰神经元。该神经元表现出对刺激的刺激反应（例如，单独的视觉，单独，体感），并且当将这些刺激组合在一起时（例如，视觉和体感）会产生综合响应。尽管许多研究已经肯定了双峰整合（尤其是在上丘中）在检测和取向行为中的作用，但尚不清楚这些戏剧性响应的变化如何影响皮质感知功能的更微妙的活性水平。为此，在皮质中进行多感觉处理的最新直接检查已经确定了一种“新”多感觉神经元的“新”形式（Meredith，2002； Dehner等，2004； Allman and Meredith，2007； Driver and Noesselt，2008年）。阈值多感神经元仅受到一种感觉方式而受到刺激，但是可以通过在原本无效的方式中同时刺激来调节该活性（促进或抑制）。结果，多感官体系结构的范围已经扩展了传统的双峰神经元的范围，甚至暗示了多感官收敛可能会沿着双峰集成的连续体发生，从而通过亚趋势调制到另一端的非整合性非构造性的非模式处理。由于现有的多感官处理的计算机模型不足以描述这些新发现，并且由于计算机模型可以探索不同实验上无法访问的不同生物学方案，因此本系列实验提出了对多感官处理频谱的计算检查。首先，使用128通道记录，将从感觉皮质神经元网络（包括单峰以及双峰和亚阈值类型）中获得实际体内感觉和多感官数据。其次，该生物学数据将用于计算机模拟中，该计算机模拟包含（a）集成和火焰尖峰神经元模型，b）静态和动态突触可塑性规则，（c）等级网络拓扑，包括兴奋性和抑制性神经元，以及（d）向网络连接引入随机性。这些功能中的每一个都是新颖的多感官处理模型。此外，对单模性刺激的真实神经元响应数据以及对相同刺激的组合（源自体内实验）的组合获得的数据将用于设计和验证模型。该仿真将不仅用于模拟不同形式的多感觉处理，而且还将检查它们对抑制级别和抑制源的响应，并测试多感官连续性假设的众多变化。该模型运行后，将构建网站，以允许任何人提交自己的数据来测试模型。这样，将收集和比较来自其他神经区域和物种（可能是从无脊椎动物到人类）的多感官数据，从而为多感觉处理提供了国际存储库和清算房屋。拟议研究的智力优点不仅包括使用最先进的计算理论和生物学验证的复杂而复杂的多感觉神经元和网络处理模型的开发，而且还将提供有关多感官处理本身及其与行为，感知，感知和认知的基本特性的洞察力和可检验的假设。这些努力的广泛影响将有助于朝着与人类相提并论的人工系统的最终挑战的进展，从而有助于开发有效的人工设备以在机器人和医学中应用。