Biomedical subjects
Publications and source records attributed to Valeri A Makarov.
The article makes use of three different examples (sensory information processing in the rat trigeminal complex, intracellular interaction in snail neurons and multimodal dynamics in nephron autoregulation) to demonstrate how modern approaches to time-series analysis based on the wavelet-transform can provide information about the underlying complex biological processes.
Independent component analysis (ICA) has been proven useful for suppression of artifacts in EEG recordings. It involves separation of measured signals into statistically independent components or sources, followed by rejection of those deemed artificial. We show that a "leak" of cerebral activity of interest into components marked as artificial means that one is going to lost that activity. To overcome this problem we propose a novel wavelet enhanced ICA method (wICA) that applies a wavelet thresholding not to the observed raw EEG but to the demixed independent components as an intermediate step. It allows recovering the neural activity present in "artificial" components. Employing semi-simulated and real EEG recordings we quantify the distortions of the cerebral part of EEGs introduced by the ICA and wICA artifact suppressions in the time and frequency domains. In the context of studying cortical circuitry we also evaluate spectral and partial spectral coherences over ICA/wICA-corrected EEGs. Our results suggest that ICA may lead to an underestimation of the neural power spectrum and to an overestimation of the coherence between different cortical sites. wICA artifact suppression preserves both spectral (amplitude) and coherence (phase) characteristics of the underlying neural activity.
We provide here a thorough analysis of the interplay between anharmonic lattice dynamics (with exponential repulsion between units) and electric conduction in a driven-dissipative electrically charged one-dimensional system. First, we delineate the ranges of parameter values where, respectively, subsonic and supersonic wave solitons are possible along the lattice. Then, we study the consequences of the soliton-mediated coupling of light negative to heavy positive charges (lattice units). In the presence of an external electric field we obtain the current-field characteristics for a wide range of values of all parameters defining the system. Finally, we discuss the conditions for an Ohmic-non-Ohmic transition of the electric current as the electric field strength is varied.
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.
A model is presented that simulates the process of neuronal synchronization, formation of coherent activity clusters and their dynamic reorganization in the olivo-cerebellar system. Three coupled 2D lattices dealing with the main cellular groups in this neuronal circuit are used to model the dynamics of the excitatory feedforward loop linking the inferior olive (IO) neurons to the cerebellar nuclei (CN) via collateral axons that also proceed to terminate as climbing fiber afferents to Purkinje cells (PC). Inhibitory feedback from the CN-lattice fosters decoupling of units in a vicinity of a given IO neuron. It is shown that noise-sustained oscillations in the IO-lattice are capable to synchronize and generate coherent firing clusters in the layer accounting for the excitable collateral axons. The model also provides phase resetting of the oscillations in the IO-lattices with transient silent behavior. It is also shown that the CN-IO feedback leads to transient patterns of couplings in the IO and to a dynamic control of the size of clusters.