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Identification of Functionally Interconnected Neurons Using Factor Analysis
Joint Authors
Soletta, Jorge H.
Farfán, Fernando D.
Albarracín, Ana L.
Pizá, Alvaro G.
Lucianna, Facundo A.
Felice, Carmelo J.
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-04-16
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
The advances in electrophysiological methods have allowed registering the joint activity of single neurons.
Thus, studies on functional dynamics of complex-valued neural networks and its information processing mechanism have been conducted.
Particularly, the methods for identifying neuronal interconnections are in increasing demand in the area of neurosciences.
Here, we proposed a factor analysis to identify functional interconnections among neurons via spike trains.
This method was evaluated using simulations of neural discharges from different interconnections schemes.
The results have revealed that the proposed method not only allows detecting neural interconnections but will also allow detecting the presence of presynaptic neurons without the need of the recording of them.
American Psychological Association (APA)
Soletta, Jorge H.& Farfán, Fernando D.& Albarracín, Ana L.& Pizá, Alvaro G.& Lucianna, Facundo A.& Felice, Carmelo J.. 2017. Identification of Functionally Interconnected Neurons Using Factor Analysis. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1141130
Modern Language Association (MLA)
Soletta, Jorge H.…[et al.]. Identification of Functionally Interconnected Neurons Using Factor Analysis. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1141130
American Medical Association (AMA)
Soletta, Jorge H.& Farfán, Fernando D.& Albarracín, Ana L.& Pizá, Alvaro G.& Lucianna, Facundo A.& Felice, Carmelo J.. Identification of Functionally Interconnected Neurons Using Factor Analysis. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1141130
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141130