A method for determining neural connectivity and inferring the underlying network dynamics using extracellular spike recordings.
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.
Makarov
Valeri A
VA
Neuroscience Laboratory, Department of Applied Mathematics, School of Optics, Universidad Complutense de Madrid, Avda. Arcos de Jalon s/n, 28037 Madrid, Spain. vmakarov@opt.ucm.es
Panetsos
Fivos
F
de Feo
Oscar
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D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D002490:Central Nervous System / Q000502:physiology; D004594:Electrophysiology / Q000379:methods*; D019706:Excitatory Postsynaptic Potentials / Q000502:physiology; D006801:Humans; D009433:Neural Inhibition / Q000502:physiology; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D009482:Neurophysiology / Q000379:methods*; D009435:Synaptic Transmission / Q000502:physiology