Person:
Panetsos Petrova, Fivos

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First Name
Fivos
Last Name
Panetsos Petrova
URI
https://hdl.handle.net/20.500.14352/79874
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Óptica y Optometría
Department
Biodiversidad, Ecología y Evolución
Area
Matemática Aplicada
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    A method for determining neural connectivity and inferring the underlying network dynamics using extracellular spike recordings
    (Journal of Neuroscience Methods, 2005) Makarov Slizneva, Valeriy; Panetsos Petrova, Fivos; De Feo, Óscar
    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.
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    Separation of extracellular spikes: When wavelet based methods outperform the principle component analysis
    (Mechanisms, Symbols, and Models Underlying Cognition, 2005) Makarov Slizneva, Valeriy; Pavlov, Alexey N.; Makarova, J.; Panetsos Petrova, Fivos; Mira, J; Álvarez, JR
    Spike separation is a basic prerequisite for analyzing of the cooperative neural behavior and neural code when registering extracellularly. Final performance of any spike sorting method is basically defined by the quality of the discriminative features extracted from the spike waveforms. Here we discuss two features extraction approaches: the Principal Component Analysis (PCA), and methods based on the Wavelet Transform (WT). We show that the WT based methods outperform the PCA only when properly tuned to the data, otherwise their results may be comparable or even worse. Then we present a novel method of spike features extraction based on a combination of the PCA and continuous WT. Our approach allows automatic tuning of the wavelet part of the method by the use of knowledge obtained from the PCA. To illustrate the methods strength and weakness we provide comparative examples of their performances using simulated and experimental data.