Neural connectivity

A method for determining neural connectivity and inferring the underlying networknext term dynamics using extracellular spike recordingsValeri A. Makarova, Corresponding Author Contact Information, E-mail The Corresponding Author, Fivos Panetsosa and Oscar de FeobaNeuroscience Laboratory, Department of Applied Mathematics, School of Optics, Universidad Complutense de Madrid, Avda. Arcos de Jalon s/n, 28037 Madrid, SpainbLaboratory of Nonlinear Systems, Swiss Federal Institute of Technology Lausanne; EPFL-IC-LANOS, CH-1015 Lausanne, SwitzerlandReceived 11 October 2004; revised 10 November 2004; accepted 12 November 2004. Available online 21 December 2004.AbstractIn the present paper we propose a novel method for the identification and modeling of neural previous termnetworksnext term using extracellular spike recordings. We create a deterministic model of the effective previous termnetwork,next term whose dynamic behavior fits experimental data. The previous termnetworknext term 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 previous termnetworks.next termKeywords: Neural circuits; Spike trains; Connectivity identification; previous termNetworknext term modeling

ScienceDirect - Journal of Neuroscience Methods : A method for determining neural connectivity and inferring the underlying network dynamics using extracellular spike recordings