RT Journal Article
T1 A method for determining neural connectivity and inferring the underlying network dynamics using extracellular spike recordings
A1 Makarov Slizneva, Valeriy
A1 Panetsos Petrova, Fivos
A1 De Feo, Óscar
AB In the present paper we propose a novel method for the identification and modeling of neural networks using extracellular spike recordings. We create a deterministic model of the effective network, whose dynamic behavior fits experimental data. The network obtained by our method includes explicit mathematical models of each of the spiking neurons and a description of the effective connectivity between them. Such a model allows us to study the properties of the neuron ensemble independently from the original data. It also permits to infer properties of the ensemble that cannot be directly obtained from the observed spike trains. The performance of the method is tested with spike trains artificially generated by a number of different neural networks. (c) 2004 Elsevier B.V. All rights reserved.
PB Elsevier
SN 0165-0270
YR 2005
FD 2005-06-15
LK https://hdl.handle.net/20.500.14352/50156
UL https://hdl.handle.net/20.500.14352/50156
LA eng
NO Makarov Slizneva, V., Panetsos Petrova, F., De Feo, Ó. «A Method for Determining Neural Connectivity and Inferring the Underlying Network Dynamics Using Extracellular Spike Recordings». Journal of Neuroscience Methods, vol. 144, n.o 2, junio de 2005, pp. 265-79. DOI.org (Crossref), https://doi.org/10.1016/j.jneumeth.2004.11.013.
DS Docta Complutense
RD 7 abr 2025