Identification of Functionally Interconnected Neurons Using Factor Analysis. (16th April 2017)

• Record Type: Journal Article
• Authors: Soletta, Jorge H.; Farfán, Fernando D.; Albarracín, Ana L.; Pizá, Alvaro G.; Lucianna, Facundo A.; Felice, Carmelo J.
• Other Names: Scott Rod Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2017(2017)
• Publication Date: 2017-04-16
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2017/8056141
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : 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.