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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.

摘要

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

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

DOI：
10.3389/fncir.2022.977700
发表时间：
2022
期刊：
Frontiers in neural circuits
影响因子：
3.5
作者：
通讯作者：

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