Neuronal Ensemble Modeling and Analysis with Variable Order Markov ModelsNeuronal cells (neurons) mainly transmit signals by action potentials or spikes.Neuronal electrical activity is recorded from experimental animals bymicroelectrodesplaced in specific brain areas. These electrochemical fast phenomenaoccur as all-or-none events and can be analyzed as boolean sequences. Followingthis approach, several computational analyses reported most variable neuronalbehaviors expressed through a large variety of firing patterns [13]. Thesepatternshave been modeled as symbolic strings with a number of different techniques[23, 55]The results obtained with these methods come (i) from Ventrobasal ThalamicNuclei (VB) and Somatosensory Cortex (SSI) in Chronic Pain Animals (CPAs), (ii) from Primary Visual (V1) and (SSI) in rat Cortices and, finally, (iii) fromIL human Thalamus Nuclei in patients suffering from states of disorderedconsciousnesslike Persistent Vegetative State (PVS) and Minimum Conscious State(MCS). |
Contents
Biological Background | 19 |
Computational Neuroscience Modeling | 39 |
Mathematical Modeling Methods | 53 |
Experiments | 85 |
Implementations | 91 |
Results | 95 |
Conclusions | 133 |
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Common terms and phrases
ACSs action potential analysis autocorrelation average log-loss axon behavior brain cells chaotic chronic pain chronic pain animal clustering complex compression algorithms computed context cortex cortical CPAs CR PI SC detected disorders of consciousness dynamics electrodes estimated experimental extract Figure firing patterns firing rate functional graph Hidden Markov Models I(s0 sk IEEE input intralaminar ions Kolmogorov complexity length logistic map Markov Chain Markov Models matrix norm membrane methods MUSPs neural neuronal ensembles neurons neurophysiological Neuroscience nuclei ongoing activity parameter patients PCTW PI SC SL Population coding prediction rats recordings redundancy region represents s0 sk Sample sensory signal similarity single neuron Small-World Networks SN sequences somatosensory somatosensory system spike sorting spike trains spinal cord spontaneous activity SS-I SS−I stimulus structures studies suffix tree symbolic sequence synchrony temporal autocorrelation thalamocortical thalamus tion tree source Type I intermittency VOMM