Exploring the Usefulness of Formal Concept Analysis for Robust Detection of Spatio-temporal Spike Patterns in Massively Parallel Spike Trains

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• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9717
• 期：1
• 页码：3-16
• 全文大小：1,024 KB
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• 作者单位：Alper Yegenoglu (16)
  Pietro Quaglio (16)
  Emiliano Torre (16)
  Sonja Grün (16) (17)
  Dominik Endres (18)
  
  16. Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany
  17. Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany
  18. Theoretical Neuroscience Group, Department of Psychology, Philipps-University, Marburg, Germany
  
• 丛书名：Graph-Based Representation and Reasoning
• ISBN：978-3-319-40985-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9717

文摘

The understanding of the mechanisms of information processing in the brain would yield practical impact on innovations such as brain-computer interfaces. Spatio-temporal patterns of spikes (or action potentials) produced by groups of neurons have been hypothesized to play an important role in cortical communication [1]. Due to modern advances in recording techniques at millisecond resolution, an empirical test of the spatio-temporal pattern hypothesis is now becoming possible in principle. However, existing methods for such a test are limited to a small number of parallel spike recordings. We propose a new method that is based on Formal Concept Analysis (FCA, [11]) to carry out this intensive search. We show that evaluating conceptual stability [18] is an effective way of separating background noise from interesting patterns, as assessed by precision and recall rates on ground truth data. Because of the scaling behavior of stability evaluation, our approach is only feasible on medium-sized data sets consisting of a few dozens of neurons recorded simultaneously for some seconds. We would therefore like to encourage investigations on how to improve this scaling, to facilitate research in this important area of computational neuroscience.