Recording Snail Brain Activity with a Multi-electrode Array

随着我们对网络结构和神经网络的涌现特性的理解越来越详细， 对解释和建议实验的网络模型的需求持续增长。Harirngton博士已经将64 电极平面电极阵列从蜗牛的大脑神经节记录。随着多电极阵列，博士。 哈灵顿和她的学生正在记录两种不同蜗牛模型中与感觉加工有关的神经活动 系统.其中一种模式是蜗牛，一种捕食性蜗牛，通过跟踪猎物（其他蜗牛）来跟踪猎物。 史莱姆的踪迹，用一种独特的，专门的感觉器官来探测史莱姆。哈灵顿博士和她的学生记录了 大量关于蜗牛大脑神经节活动的数据，以及蜗牛大脑神经节活动的变化， 对粘液和施加于感觉上皮的其他刺激的反应。分析数据并理解其 重要性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 作为理解神经过程的工具，将通过使用数据来告知 神经整合和决策过程的计算模型。从蜗牛神经节收集的数据 和其他蜗牛的数据正在通过三种方式进行分析：首先是使用尖峰排序程序来计数和关联 所有64个电极上的神经尖峰活动，以计算和比较尖峰频率和同步 第二种方法使用互相关来识别同步活动的变化模式。一 第三种方法将使用独立分量分析（伊卡）算法来分解在 64个电极，并识别对总信号有贡献的不同源信号。该方法已用于 在人类EEG和MEG应用中的诱发场电位的分解， 在许多方面都被我们的无脊椎记录所模仿。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性，以确定对细胞的贡献。 神经节中不同类型细胞的网络活动。结合空间和时间的数据 通过神经节的神经活动模式以及关于单个细胞的生物物理特性的信息， 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。

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.