Circuit Mechanisms Underlying Learned Changes in Persistent Neural Activity

持久神经活动习得变化背后的回路机制

基本信息

批准号：
10322719
负责人：
Emre R Aksay
金额：
$ 79.81万
依托单位：
WEILL MEDICAL COLL OF CORNELL UNIV
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-01-15 至 2024-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10322719
关键词：
Anatomy Architecture Area Behavior Benchmarking Biological Models Biophysical Process Brain Brain Stem Brain imaging Brain region Buffers Cells Cerebellum Chemosensitization Code Computer Models Coupled Data Decision Making Development Disease Drug Design Electron Microscopy Electrophysiology (science)Etiology Eye Eye Movements Functional Imaging Genetic Markers Image Information Storage Knowledge Laboratories Learning Maps Measurement Measures Mediating Memory Mental Depression Modeling Monitor Motor Motor Neurons Neurons Neurosciences Optics Pattern Physiological Population Positioning Attribute Property Psychological reinforcement Resolution Short-Term Memory Signal Transduction Site Stimulus Structure Synapses System Testing Vision Work Zebrafish activity marker analog base biophysical model cell type cognitive task connectome convolutional neural network excitatory neuron eye velocity gaze hindbrain image reconstruction improved in vivo inhibitory neuron insight motor disorder motor learning network models next generation oculomotor optical imaging predictive modeling reconstruction relating to nervous system response tool two photon microscopy two-photon virtual reality environment

项目摘要

ABSTRACT Persistent neural activity, a sustained response following brief stimuli that is observed in many brain networks, needs to be appropriately tuned to meet the exacting demands of various motor and cognitive tasks. One task that has been particularly amenable to understanding persistent neural activity is the oculomotor control of gaze position. In the oculomotor system, where persistent activity maintains the appropriate gaze angle necessary for high-acuity vision, this tuning arises in large part through an interaction loop between the cerebellum and the oculomotor neural integrator, a brainstem structure that temporally integrates eye velocity commands to generate eye position encoding signals. Whereas we understand a fair bit about the coding properties and signal transformations within the cerebellum and especially the neural integrator proper, the interactions between these key brain centers is poorly understood. Here we aim to close this gap through an integrative approach using a combination of whole-network two-photon imaging, targeted optical perturbations, serial-section electron microscopy, in-vivo electrophysiology, and next-generation network modeling. In Aim 1, we will combine volumetric imaging of neuronal dynamics in the zebrafish with learning and targeted perturbations to examine the causal relationships between cerebellar and integrator populations. In Aim 2, we will perform high-resolution serial-section electron microscopy of a functionally imaged brain to gain insight into the global circuit's anatomical connectivity. In Aim 3, we will directly fit a network model to these dynamical and structural data to find the physiological interaction patterns in the circuit and predict the sites of plasticity. In Aim 4, we will probe predicted sites of plasticity using cell-targeted optical stimulations and whole-cell patch recordings. These tools and results will enable concrete predictions about the circuit mechanisms tuning persistent neural activity, guide understanding of the modular loops between the cerebellum and a wide range of other brain areas, and serve as a benchmark for efforts to understand the dynamics and plasticity of interactions across the brain.

抽象的持续的神经活动，这是一种在短暂刺激之后的持续反应，在许多人中观察到大脑网络需要适当调整以满足各种电动机的严格需求和认知任务。一项特别适合理解持久性的任务神经活动是凝视位置的动眼控制。在动眼系统中持续活动保持高敏视觉所需的适当目光角度调整大部分通过小脑和眼动物之间的互动回路。神经积分器，一种脑干结构，将眼速命令暂时整合到生成编码信号的眼睛位置。而我们对编码有很大了解小脑，尤其是神经积分器内的特性和信号转换适当地，这些关键的大脑中心之间的相互作用知之甚少。在这里，我们的目标是通过整个网络两光量的组合通过集成方法来缩小这一差距成像，靶向光扰动，串行部分电子显微镜，体内电生理学和下一代网络建模。在AIM 1中，我们将结合体积斑马鱼中神经元动力学的成像，并通过学习和有针对性的扰动来检查小脑和整合物种群之间的因果关系。在AIM 2中，我们将表演功能成像的大脑的高分辨率系列电子显微镜以获得洞察力进入全球电路的解剖连接。在AIM 3中，我们将直接拟合网络模型这些动态和结构性数据，以在电路中找到生理相互作用模式预测可塑性的位置。在AIM 4中，我们将使用靶向细胞的探测可塑性的探测位点光学刺激和全细胞斑块记录。这些工具和结果将启用混凝土关于电路机制调整持续性神经活动的预测，指导对小脑和许多其他大脑区域之间的模块化环，并用作努力了解整个大脑相互作用的动态和可塑性的基准。

项目成果

期刊论文数量（18）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Population-scale organization of cerebellar granule neuron signaling during a visuomotor behavior.

DOI：
10.1038/s41598-017-15938-w
发表时间：
2017-11-24
期刊：
Scientific reports
影响因子：
4.6
作者：
Sylvester SJG;Lee MM;Ramirez AD;Lim S;Goldman MS;Aksay ERF
通讯作者：
Aksay ERF

In situ X-ray-assisted electron microscopy staining for large biological samples.

DOI：
10.7554/elife.72147
发表时间：
2022-10-20
期刊：
eLife
影响因子：
7.7
作者：
Ströh S;Hammerschmith EW;Tank DW;Seung HS;Wanner AA
通讯作者：
Wanner AA

Learning and Segmenting Dense Voxel Embeddings for 3D Neuron Reconstruction.

DOI：
10.1109/tmi.2021.3097826
发表时间：
2021-12
期刊：
IEEE transactions on medical imaging
影响因子：
10.6
作者：
Lee K;Lu R;Luther K;Seung HS
通讯作者：
Seung HS

Cyclic structure with cellular precision in a vertebrate sensorimotor neural circuit.

脊椎动物感觉运动神经回路中具有细胞精度的循环结构。

DOI：
10.1016/j.cub.2023.05.010
发表时间：
2023
期刊：
Current biology : CB
影响因子：
0
作者：
Yang,Runzhe;Vishwanathan,Ashwin;Wu,Jingpeng;Kemnitz,Nico;Ih,Dodam;Turner,Nicholas;Lee,Kisuk;Tartavull,Ignacio;Silversmith,WilliamM;Jordan,ChrisS;David,Celia;Bland,Doug;Sterling,Amy;Goldman,MarkS;Aksay,EmreRF;Seung,HSeb
通讯作者：
Seung,HSeb

Igneous: Distributed dense 3D segmentation meshing, neuron skeletonization, and hierarchical downsampling.