Recording Snail Brain Activity with a Multi-electrode Array

用多电极阵列记录蜗牛大脑活动

• 批准号: 7769886
• 负责人: MELISSA A HARRINGTON
• 金额: $ 14.01万
• 依托单位: DELAWARE STATE UNIVERSITY
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7769886
• 关键词: Algorithms; Behavior; Biological Models; Brain; Cells; Cerebrum; Computer Architectures; Computer Simulation; Data; Data Analyses; Decision Making; Development; Electrodes; Electroencephalography; Electrophysiology (science); Epithelium; Frequencies; Ganglia; Human; Individual; Invertebrates; Mathematical Biology; Modeling; Myxoid cyst; Nerve; Nervous system structure; Neural Network Simulation; Pattern; Process; Property; Research; Research Project Grants; Sense Organs; Sensory; Sensory Process; Signal Transduction; Snails; Sorting - Cell Movement; Source; Stimulus; Students; Techniques; Technology; Underrepresented Minority; cell type; computational network modeling; independent component analysis; insight; network models; neural model; neural patterning; neurophysiology; programs; relating to nervous system; research study; response; tool

As our understanding of network architecture and the emergent properties of nerve networks gets ever more detailed, the need for network models to explain and suggest experiments continues to grow. Dr. Harirngton has adapted a 64 electrode planar electrode array to record from the cerebral ganglia of snails. With the multi electrode array, Dr. Harrington and her students are recording the neural activity related to sensory processing in two different snail model systems. One of these models is the wolfsnail, a predatory snail that tracks its prey (other snails) by following their slime trails, detecting the slime with a unique, specialized sense organ. Dr. Harrington and her students have recorded vast amounts of data about the activity of the cerebral ganglia in the snails and changes that occur in that activity in response to slime and other stimuli applied to the sensory epithelia. Analyzing the data and understanding its significance is a major computational challenge requiring the tools of mathematical biology. The usefulness of the data as a tool for understanding neural processes will be greatly enhanced by using the data to inform the development of computational models of neural integration and decision-making processes. The data collected from wolfsnail ganglia and those of other snails are being analyzed in three ways: first with a spike sorting program that counts and correlates neural spike activity across all 64 electrodes to calculate and compare spike frequency and synchronization across the electrode array, A second approach uses cross-correlation to identify changing patterns of synchronized activity. A third approach will use an Independent Component Analysis (ICA) algorithm to decompose the activity recorded at the 64 electrodes and identify different source signals contributing to the total signal. This approach has been used for decomposition of evoked field potentials in human EEG and MEG applications, neural recording techniques that are emulated by our invertebrate recordings in many respects. The data collected with the electrode array will be correlated with the activity of individual cells recorded electrophysiologically in order to determine the contributions to the network activity attributable to the different types of cells in the ganglia. Combining data about the spatial and temporal pattern of neural activity across the ganglia with information about the biophysical propoerties of individual cells will enable us to develop network and computational models of sensory processing in an invertebrate model system.

随着我们对网络体系结构和神经网络的新兴特性的理解变得越来越详细， 对网络模型来解释和建议实验的需求持续增长。哈林顿博士改编了一部 电极平面电极阵列从钉螺的脑神经节记录。使用多电极阵列，Dr。 哈林顿和她的学生正在记录两个不同蜗牛模型中与感觉处理相关的神经活动 系统。其中一个模型是狼蜗牛，这是一种捕食性蜗牛，通过跟踪它们的猎物(其他蜗牛) 粘液跟踪，用一个独特的、专门的感官检测粘液。哈林顿博士和她的学生记录了 关于蜗牛大脑神经节活动的大量数据以及在该活动中发生的变化 对粘液和其他刺激的反应作用于感觉上皮细胞。分析数据并了解其 意义是一个主要的计算挑战，需要数学生物学的工具。数据的有用性 作为了解神经过程的工具，通过使用数据来告知发展将大大增强 神经整合和决策过程的计算模型。从狼螺神经节收集的数据 而对其他蜗牛的分析有三种方式：首先，使用一个可以计数和关联的钉子分类程序 所有电极上的神经元棘波活动，以计算和比较棘波频率和跨 电极阵列，第二种方法使用互相关来识别同步活动的变化模式。一个 第三种方法将使用独立分量分析(ICA)算法来分解记录在 电极和辨认不同来源的信号对总信号有贡献。这种方法已经被用于 人类脑电和脑磁图应用中诱发场势的分解，神经记录技术是 在许多方面被我们的无脊椎动物录音所效仿。利用电极阵列收集的数据将被关联 通过电生理学记录单个细胞的活动，以确定对 网络活动归因于神经节中不同类型的细胞。结合关于空间和时间的数据 通过神经节的神经活动模式和关于单个细胞的生物物理特性的信息将 使我们能够在无脊椎动物模型系统中开发感觉处理的网络和计算模型。