CRCNS: Bayesian inference in spiking sensory neurons

CRCNS：尖峰感觉神经元的贝叶斯推理

• 批准号: 8837236
• 负责人: LARRY F HOFFMAN
• 金额: $ 18.85万
• 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 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8837236
• 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; 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; Relative (related person); 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; 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）：贝叶斯脑假说认为，人类和动物的神经系统以概率分布的方式传递和处理信息，这一点有越来越多的心理物理证据。然而，很少有研究试图研究贝叶斯推理在感觉加工的早期阶段。通过这项研究，我们建议在内耳前庭系统的初级传入神经元和次级神经元中实施这样的测试。我们将探索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。我们的方法的优势和新颖之处在于，我们提出了一个可测试的理论，关于头部状态信息如何分别被外围和中枢感觉神经元表示为峰值测量密度（即SMDs）和峰值后验密度（即spd）。此外，我们提出了一个假设，即通过粒子滤波器的神经模拟，用新的感觉数据（即通过主要传入smd）更新头部运动状态的贝叶斯后验的中心表示。粒子滤波为检测二阶前庭神经元动态放电是否代表头部运动状态空间的离散轨迹提供了计算框架。这些实验将在晚期爪蟾幼虫中进行，从中可以通过对自由游泳动物的录像分析明确确定头部运动状态的自然分布。这种自然分布将用于推导粒子滤波模型中的动态先验，也可作为转台刺激的基础，可在美国和德国的实验室中分别用于记录初级和二级神经元的诱发放电。我们将利用脊髓前根可记录的模拟游泳信号，通过呈现与模拟运动干扰拷贝相冲突的头部运动刺激，来修改运动和头部运动状态之间的自然相位关系。我们假设这种异常表征将导致动态后验通过集体spd的中央表征出现可预测的错误，这将为尖峰感觉神经元中贝叶斯状态估计的计算框架提供强有力的支持。