URoL:EN: Learning the Rules of Neuronal Learning

URoL:EN：学习神经元学习的规则

• 批准号: 2133769
• 负责人: Pamela Abshire
• 金额: $ 294.91万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2133769&HistoricalAwards=false
• 关键词: URoL; EN; Learning; Rules; Neuronal

This project will explore the rules of life of neuronal networks in an engineered microenvironment. The networks of neurons in biological brains routinely solve challenging problems in pattern recognition and classification more efficiently and with better performance than man-made computers. This has inspired algorithms and engineering models of computing behind the sophisticated voice recognition and computer vision available today, yet there has been limited progress towards understanding the physical mechanisms within a living neuron and its neighbors underlying its remarkable capabilities. This project brings together recent technological advances in patterning, recording, stimulation, and genetic manipulation of neurons to study how to nurture a healthy culture of neurons while continuously observing and stimulating them at a scale fine enough to uncover how the individual parts of a single neuron contribute to the overall learning and computation of the network. The goal is a proof-of-concept demonstration of a programmable computation by neurons in a dish. If successful, this will have significant implications for both the scientific understanding of natural neuronal computation and would introduce a completely new set of engineering tools for interacting with living neurons and exploring what is computationally possible. New understanding from the neuronal rules of life may impact artificial intelligence, robotics, and neural prosthesis. This project will seek to engage the public with new insights and results while providing the software and datasets as a scientific resource to allow other investigators to verify and extend the results. Neuronal computation is distributed and dynamic in nature. This project will investigate the rules of life of neuronal networks at an unprecedented spatial and temporal scale within an engineered microenvironment by extending existing technologies to demonstrate trainable spatiotemporal pattern recognition by small networks of cultured neurons. To accomplish this, a multidisciplinary team of engineers and biologists will adapt emerging techniques for culturing neurons in patterned microenvironments and for recording/stimulating neurons using high density microelectrode arrays and optogenetics. The primary scientific hypothesis is that a single neuron, when presented with a spatiotemporal pattern of input spikes distributed on its dendritic tree, can be trained to respond highly selectively to one pattern out of many distractor stimuli. This research will be organized along five distinct thrusts: 1) development of a stable, non-bursting, neuron/glia co-culture that receives persistent background stimulation to avoid sensory deprivation, 2) identification and localization of network synaptic connectivity between cells and the modification of synaptic strength based on spike-timing-dependent plasticity, 3) characterization of computational primitives observed in the dendritic tree, 4) development of training protocols for single-neuron, selective learning of spatiotemporal patterns in simulation, and 5) training of a live, cultured neuron to selectively respond to a target spatiotemporal pattern. The experiments will make use of high-density microelectrode arrays and a high-resolution fluorescence microscope with the capability to project patterned light for optogenetic stimulation of cells. This research will establish fundamental and practical understanding of how the neurons individually and collectively respond to their dynamic environment, develop predictive models of the response, and develop control laws to tune the network for specific spatiotemporal pattern recognition tasks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将探索神经元网络在工程微环境中的生命规则。生物大脑中的神经元网络通常比人造计算机更有效地解决模式识别和分类中的挑战性问题，并且具有更好的性能。这启发了当今先进的语音识别和计算机视觉背后的计算算法和工程模型，但在理解活神经元及其邻居的物理机制方面进展有限。该项目汇集了神经元的模式化，记录，刺激和遗传操作方面的最新技术进展，以研究如何培养健康的神经元文化，同时以足够精细的尺度持续观察和刺激它们，以揭示单个神经元的各个部分如何有助于网络的整体学习和计算。其目标是通过培养皿中的神经元进行可编程计算的概念验证。如果成功，这将对自然神经元计算的科学理解产生重大影响，并将引入一套全新的工程工具，用于与活神经元进行交互并探索计算可能性。对生命神经规则的新理解可能会影响人工智能，机器人和神经假体。该项目将寻求让公众参与新的见解和结果，同时提供软件和数据集作为科学资源，让其他研究人员验证和扩展结果。 神经元计算在本质上是分布式和动态的。该项目将通过扩展现有技术，在工程微环境中以前所未有的时空尺度研究神经元网络的生活规则，以证明培养神经元的小型网络的可训练时空模式识别。为了实现这一目标，一个由工程师和生物学家组成的多学科团队将采用新兴技术，在图案化的微环境中培养神经元，并使用高密度微电极阵列和光遗传学记录/刺激神经元。主要的科学假设是，一个神经元，当呈现一个时空模式的输入尖峰分布在其树突树，可以训练高度选择性地响应一个模式的许多分心刺激。这项研究将按照沿着五个不同的方向组织：1）开发稳定的、非爆发的神经元/神经胶质共培养物，其接受持续的背景刺激以避免感觉剥夺，2）识别和定位细胞之间的网络突触连接性，以及基于尖峰时间依赖性可塑性的突触强度的修改，3）表征在树突树中观察到的计算基元，4）开发用于单神经元的训练协议，在模拟中选择性地学习时空模式，以及5）训练活的、培养的神经元以选择性地响应于目标时空模式。这些实验将利用高密度微电极阵列和高分辨率荧光显微镜，该显微镜能够投射图案化的光来刺激细胞。这项研究将建立神经元如何单独和集体响应其动态环境的基本和实际的理解，开发响应的预测模型，并开发控制法律，以调整特定时空模式识别任务的网络。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。