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Investigating the Collective Behavior of Neural Networks: A Review of Signal Processing Approaches

Investigating the Collective Behavior of Neural Networks: A Review of Signal Processing Approaches

A. Maffezzoli
Copyright: © 2009 |Pages: 15
ISBN13: 9781605660769|ISBN10: 1605660760|EISBN13: 9781605660776
DOI: 10.4018/978-1-60566-076-9.ch032
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MLA

Maffezzoli, A. "Investigating the Collective Behavior of Neural Networks: A Review of Signal Processing Approaches." Handbook of Research on Systems Biology Applications in Medicine, edited by Andriani Daskalaki , IGI Global, 2009, pp. 541-555. https://doi.org/10.4018/978-1-60566-076-9.ch032

APA

Maffezzoli, A. (2009). Investigating the Collective Behavior of Neural Networks: A Review of Signal Processing Approaches. In A. Daskalaki (Ed.), Handbook of Research on Systems Biology Applications in Medicine (pp. 541-555). IGI Global. https://doi.org/10.4018/978-1-60566-076-9.ch032

Chicago

Maffezzoli, A. "Investigating the Collective Behavior of Neural Networks: A Review of Signal Processing Approaches." In Handbook of Research on Systems Biology Applications in Medicine, edited by Andriani Daskalaki , 541-555. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-076-9.ch032

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Abstract

In this chapter, authors review main methods, approaches, and models for the analysis of neuronal network data. In particular, the analysis concerns data from neurons cultivated on Micro Electrode Arrays (MEA), a technology that allows the analysis of a large ensemble of cells for long period recordings. The goal is to introduce the reader to the MEA technology and its significance in both theoretical and practical aspects of neurophysiology. The chapter analyzes two different approaches to the MEA data analysis: the statistical methods, mainly addressed to the network activity description, and the system theory methods, more dedicated to the network modeling. Finally, authors present two original methods, introduced by their selves. The first method involves innovative techniques in order to globally quantify the degree of synchronization and inter-dependence on the entire neural network. The second method is a new geometrical transformation, performing very fast whole-network analysis; this method is useful for singling out collective-network behaviours with a low-cost computational effort. The chapter provides an overview of methods dedicated to the quantitative analysis of neural network activity measured through MEA technology. Until now many efforts were devoted to biological aspects of this problem without taking in to account the computational and methodological signal processing questions. This is precisely what the authors try to do with their contribution, hoping that it could be a starting point in an interdisciplinary cooperative research approach.

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