CRCNS: Bayesian inference in spiking sensory neurons

CRCNS：尖峰感觉神经元的贝叶斯推理

• 批准号: 9124841
• 负责人: LARRY F HOFFMAN
• 金额: $ 17.59万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-15 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9124841
• 关键词: Acceleration; Address; Afferent Neurons; Algorithms; Animals; Attitude; Bayesian Analysis; Behavior; Brain; Brain Stem; Caliber; Code; Collaborations; Computers; Conflict (Psychology); Data; Differential Equation; Elements; Equation; Exposure to; External Canal; Family; Fostering; Friendships; Germany; Goals; Head; Head Movements; Heterogeneity; Human; In Situ; Individual; International; Investigation; K-Series Research Career Programs; Label; Laboratories; Labyrinth; Larva; Lead; Likelihood Functions; Location; Maps; Measurement; Measures; Mediating; Methods; Minority Access; Modeling; Motor; Motor Activity; Movement; Nerve; Nervous system structure; Neurons; New Zealand; Pattern; Peripheral; Phase; Population; Principal Investigator; Probability; Process; Property; Psychophysics; Recording of previous events; Recruitment Activity; Research; Role; Rotation; Sampling; Sampling Biases; Semicircular canal structure; Sensory; Sensory Process; Signal Transduction; Source; Specific qualifier value; Spinal; Spinal nerve root structure; Staging; Statistical Distributions; Statistical Models; Stimulus; Stochastic Processes; Structure; Students; Swimming; Synapses; System; Tadpoles; Techniques; Testing; Time; Training; Update; Validation; Ventral Roots; Vestibular nucleus structure; Work; Xenopus; analog; base; career; computational neuroscience; computer framework; density; discount; expectation; experience; graduate student; information processing; insight; interdisciplinary approach; kinematics; multidisciplinary; novel; particle; peer; programs; relating to nervous system; research study; response; sensory system; summer research; theories; two-dimensional; undergraduate student

DESCRIPTION (provided by applicant): The Bayesian brain hypothesis asserts that nervous systems in humans and animals transmit and process information as probability distributions, for which there is a growing body of psychophysical evidence. However, few investigations have endeavored to investigate Bayesian inference in early stages of sensory processing. Through this investigation we propose to implement such a test in primary afferent and secondary neurons of the inner ear vestibular system. We will explore afferent circuits of the horizontal semicircular canal, a model sensory system dedicated to coding and processing head kinematic state. The strength and novelty of our approach is that we advance a testable theory about how head state information may be represented by peripheral and central sensory neurons as spike measurement densities (i.e. SMDs) and spike posterior densities (i.e. SPDs) respectively. Furthermore, we submit the hypothesis that the central representation of the Bayesian posterior of head kinematic state is updated with new sense data (i.e. by primary afferent SMDs) via a neural analog of a particle filter. The particle filter provides the computational framework to tes whether the dynamic discharge of second-order vestibular neurons represent discrete loci of head kinematic state space. These experiments will be conducted in late-stage Xenopus larvae, from which the natural distribution of head kinematic states can be explicitly determined from videographic analysis of free-swimming animals. This natural distribution will be used to derive the dynamic prior within the particle filter model, and also serve as the basis of turntable stimul that can be used in the laboratories in the US and Germany to record evoked discharge from primary and second-order neurons, respectively. We will utilize the fictive swimming signals recordable from spinal ventral roots to modify the natural phase relationship between locomotor and head movement states by presenting head movement stimuli that conflict with the fictive motor efference copy. We hypothesize that such anomalous representations will lead to predictable errors in the central representation of the dynam-ic posterior through the collective SPDs, which would render strong support of a computational framework of Bayesian state estimation in spiking sensory neurons. Intellectual Merit: The intellectual merit of this proposal is harbored in the direct testing of Bayesian inference in nervous systems through direct neurophysiologic methods. Our goal is to test whether the Bayesian particle filter model extends well beyond just another way of describing spatial and temporal patterns of activity in vestibular neurons. Rather, we posit that i can predict and explain them, thereby advancing a compelling neurocomputational model of Bayesian inference using natural neuronal components. These experiments will also provide new insights into the role of dynamic heterogeneity among vestibular afferent neurons, which will undoubtedly lead to new research strategies that will ameliorate our understanding of vestibular sensory coding. Broader Impacts: The research encompasses broader impacts that include the integrative electrophysiologic and computational neuroscience training for individuals at the postdoctoral, graduate, and undergraduate levels. The postdoctoral scholar and graduate students will receive an intensely multidisciplinary experiences, with extraordinary opportunities for international collaboration with peers in the US, Germany, and New Zealand. They will receive rigorous exposure to the theoretical and computational aspects of this project. Undergraduate students will be recruited from across the nation as summer research fellows of the Minority Access to Research Careers program. These students, as well as other undergraduate associates participating during the course of the academic year, will have opportunities to engage in laboratory work addressing the multidisciplinary approaches involved in this project. We propose that this will have a significant impact in broadening their perspective of neuroscientific investigation. We have implemented a plan that will enable direct assessments of the undergraduate students' activities in the project, as well as assessments of the research program's impact upon their attitudes and outlook toward creative scientific endeavors and careers. Our goal is that the integrated approach and international friendships fostered by this project will positively impact their confidence and perspective concerning their own scientific creative capabilities.

描述（由申请人提供）：贝叶斯大脑假说认为人类和动物的神经系统以概率分布的形式传输和处理信息，对此有越来越多的心理物理学证据。然而，很少有调查已经结束，调查贝叶斯推理的早期阶段的感觉加工。通过这项调查，我们建议实施这样的测试在初级传入和次级神经元的内耳前庭系统。我们将探讨水平半规管的传入回路，这是一个专门编码和处理头部运动状态的模型感觉系统。我们的方法的强度和新奇是，我们提出了一个可测试的理论，头部状态信息如何可以表示为穗测量密度（即SMD）和穗后密度（即SPD）分别由外周和中枢感觉神经元。此外，我们提出的假设，贝叶斯后的头部运动状态的中央表示更新与新的感觉数据（即由初级传入SMD）通过神经模拟的粒子滤波器。粒子滤波器提供了计算框架，以测试二阶前庭神经元的动态放电是否代表头部运动状态空间的离散轨迹。这些实验将进行后期爪蟾幼虫，从头部运动状态的自然分布可以明确地确定从视频分析自由游泳的动物。该自然分布将用于导出粒子滤波模型内的动态先验，并且还用作转盘刺激的基础，转盘刺激可用于美国和德国的实验室中，以分别记录来自初级和二级神经元的诱发放电。我们将利用虚构的游泳信号记录从脊髓腹根修改运动和头部运动状态之间的自然相位关系，提出头部运动刺激，与虚构的运动传出副本冲突。我们假设，这种异常的表示将导致可预测的错误，在中央表示的动态后，通过集体SPD，这将提供强有力的支持的计算框架的贝叶斯状态估计尖峰感觉神经元。 智力优势：这个建议的智力价值在于通过直接的神经生理学方法直接测试神经系统中的贝叶斯推理。我们的目标是测试贝叶斯粒子滤波模型是否远远超出了描述前庭神经元活动的空间和时间模式的另一种方式。相反，我们认为我可以预测和解释它们，从而提出了一个令人信服的神经计算模型贝叶斯推理使用自然神经元组件。这些实验也将为前庭传入神经元之间的动态异质性的作用提供新的见解，这无疑将导致新的研究策略，这将改善我们对前庭感觉编码的理解。 更广泛的影响：该研究涵盖了更广泛的影响，包括对博士后，研究生和本科生水平的个人进行综合电生理学和计算神经科学培训。博士后学者和研究生将获得强烈的多学科经验，与美国，德国和新西兰的同行进行国际合作的绝佳机会。他们将接受严格的接触到这个项目的理论和计算方面。本科生将从全国各地招募，作为少数民族获得研究职业计划的暑期研究员。这些学生，以及其他本科生的同事在学年的过程中参与，将有机会从事实验室工作，解决在这个项目中涉及的多学科方法。我们认为这将对拓宽他们的神经科学研究视角产生重大影响。我们已经实施了一项计划，这将使本科生的活动在项目中的直接评估，以及对他们的态度和展望创造性的科学努力和职业生涯的研究计划的影响评估。我们的目标是，该项目所培养的综合方法和国际友谊将积极影响他们对自己的科学创造能力的信心和看法。