A functional model based on single unit recordings from Parkinsonian brain

Artificial neuronal clusters are arranged and linearly filtered to generate signals similar to those recorded from the mid-brain regions of patients with Parkinson's disease. The goal of the research is to construct a model containing information about several aspects of recording from a neuron...

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Bibliographic Details
Published in2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA pp. 30 - 34
Main Authors Leondopulos, S., Micheli-Tzanakou, E.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2004
Subjects
Brain modeling
Conductivity
Electroencephalography
Filtering
Frequency domain analysis
Impedance
Independent component analysis
Low pass filters
Neurons
Nonlinear filters
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Summary:Artificial neuronal clusters are arranged and linearly filtered to generate signals similar to those recorded from the mid-brain regions of patients with Parkinson's disease. The goal of the research is to construct a model containing information about several aspects of recording from a neuronal cluster in-vivo. In particular, these include: number (or size) of significant neurons in the cluster, effective filtering characteristics of brain tissue between the recording electrode and each neuron, and spiking frequency of each neuron. Furthermore, models of varying size are generated based on single-unit recordings from the human brain. Results of simulations are presented and compared.
ISBN:0780383419
9780780383418
DOI:10.1109/CIMSA.2004.1397224