Study Of Spontaneous Activity Of Engineered Neural Networks

1. Study Of Spontaneous Activity Of Engineered Neural Networks By: Gil Topman Instructors: Mr. Nitzan Herzog and Prof. Yael Hanein Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Israel Biologic setup - Movie acquisition Movie temporal statistical properties extraction Neuron Segmentation Neurons activity extraction Abstract Spontaneous activity of neural networks is studied using calcium imaging. In calcium imaging a fluorescence dye sensitive to calcium ions is added to the culture and fluorescence microscope is used to acquire the signal. When an action potential is fired the calcium concentration inside the neuron rises and recorded by the microscope as a movie. The raw movie data is then processed to extract the calcium activity of each neuron in the culture. In this project we develop methods to process huge amounts of movie data efficiently, and automate the process allowing an objective extraction of calcium activity. The calcium activity can be further studied and correlated with the electrical signal of the neurons, recorded via use of planar-microelectrodes. Data processing flow Neuron Segmentation Conclusions Optimization The neuron activity extraction algorithm was optimized and its execution time on a standard movie reduced from 40 min to 4 min. Two types of optimization were applied: Direct optimization improved execution time times 3.5 to 12 min. Parallelization to 4 cores improved execution time times 2.8 to 4 min. In addition a semaphore was used to synchronize hard-disk access. Image Segmentation stages: Global threshold selected from 12 threshold algorithms candidates. Separate connected neurons using a local threshold using a specially designed algorithm. Apply connected components algorithm. Threshold algorithms scores Separation algorithm results • We successfully developed capability to process long movies in matlab extending the research boundaries. • Neuron segmentation algorithm detects 88% of the neurons with their accurate shape and size allowing to research their effects on the activity. • Optimization of the data processing flow reduced analysis time to a few minutes allowing to perform significantly more data analysis experiments. Separation algorithm result Intermodes global threshold result