Artículos de revistas
Identification of Functionally Interconnected Neurons Using Factor Analysis
Fecha
2017-04Registro en:
Soletta, Jorge Humberto; Farfan, Fernando Daniel; Albarracin, Ana Lia; Pizá, Alvaro Gabriel; Lucianna, Facundo Adrián; et al.; Identification of Functionally Interconnected Neurons Using Factor Analysis; Hindawi Publishing Corporation; Computational Intelligence and Neuroscience; 2017; 4-2017; 1-11
1687-5265
1687-5273
CONICET Digital
CONICET
Autor
Soletta, Jorge Humberto
Farfan, Fernando Daniel
Albarracin, Ana Lia
Pizá, Alvaro Gabriel
Lucianna, Facundo Adrián
Felice, Carmelo Jose
Resumen
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.