Biology subjects

Koshev, N.

Publications and source records attributed to Koshev, N..

6 recordsLinked to original sources

bioRxiv2026

Cross-excitation in dorsal root ganglia redistributes coding capacity from stimulus intensity to stimulus location

The phenomenon of cross-excitation between neurons of dorsal root ganglia (DRGs) of mammals is widely known, yet its exact mechanism remains underexplored. In this work, we analyze the phenomenon as part of the signal processing performed by the body's somatosensory system. We argue that, if the mechanism is indeed as prevalent as it is reported in experimental works, it should provide a significant computational advantage. Here, we created a model network in which sensory A-type neurons are interconnected only via a chemical agent and showed that such a connection, when fitted with parameters similar to those observed in experiments, improves the stimulus-localization properties of the ganglia at the expense of intensity-encoding accuracy. Although this exchange is near-neutral in raw bits, it becomes favorable once the logarithmic compression of intensity perception - the Weber--Fechner law - is taken into account, since the sacrificed intensity capacity is largely perceptually redundant. We therefore propose that cross-excitation is not an imperfection of somatic isolation but an effective, chemically mediated pre-processing stage that sharpens spatial localization.

neuroscience↗

bioRxiv2025

What is happening in Dorsal Root Ganglia? A computational analysis of cross-excitation phenomenon

The phenomenon of cross-excitation - interaction between neurons of a dorsal root ganglion (DRG) in intact organism, presents a great challenge since its discovery. It can not be addressed to classical neuron-neuron interactions, as the mentioned neurons are believed to be physiologically isolated by glial sheaths from one another. In this work we test existing hypotheses which explain the phenomenon from both biophysical and statistical perspectives. Our conclusion is that neither each proposed mechanism alone, nor interactions of them can adequately describe cross-excitation in normal conditions. We wish to draw greater attention towards the phenomenon, as it seems to be an important part of normal sensory information processing. Author summaryCross-excitation is a phenomenon discovered in Dorsal Root Ganglia (DRG), whereby activation of one neuron leads to increased excitability of its neighbors. As the mechanism is still poorly understood, we present a computational modelling study, in which we tested primary hypotheses which were proposed to the phenomenon. The objectives were to test two main ideas - diffusion-mediated and gap-junction mediated cross-excitation in biologically plausible setups. Firstly, we evaluated a diffusion-based hypothesis, which states that diffusion of K+ ions is the main mechanism by which cross-excitation is mediated. Our results suggest miniscule effect of ion diffusion, even if tightly-packed space of DRG is considered. Secondly, we analyzed the gap-junction hypothesis, which relates the phenomenon to physical connection between neurons glial envelopes. According to the results, this solution would require for each neuron to have an enormous amount of immediate neighbors, in order to sustain high-enough prevalence of connection. Geometrical conclusion is that, no matter how tightly packed the DRG, it is impossible to create enough contacts. As a result, our findings suggest that none of the leading hypotheses explain the phenomenon.

neuroscience↗

bioRxiv2022

Yttrium-iron garnet film magnetometer for magnetic microparticles in vivo registration studies

In the current article, we present a new kind of magnetometer for quantitative determination of magnetic objects in biological fluids and tissues. The sensor is based on yttrium-iron garnet film with optical signal registration system. Inheriting the working principle of a fluxgate magnetometers, the sensor works at a room-temperature, its wide dynamic range allows the measurements in an unshielded environment. A small size of sensitive element combined with a short recovery time after the excitation coils are off provide us with a potentially high spatial and temporal resolution of measurements. We show the feasibility of the sensor by sensing the remanent magnetization of Magnetic Nanoparticles (MNPs) both in vitro (test tubes, dry MNPs) and in vivo (local injection of the MNPs into mice).

biophysics↗

bioRxiv2022

Yttrium-iron garnet magnetometer in MEG: Advance towards multi-channel arrays

Recently, a new kind of sensor applicable in magnetoencephalography (MEG) has been presented: a solid-state yttrium-iron garnet magnetometer (YIGM). The feasibility of YIGM was proved in alpha-rhythm registration experiment. In the current paper, we propose the analysis of lead-field matrices for different possible YIGM multichannel on-scalp sensor layouts with respect to the information theory. We use real noise level of the new sensor to compute signal power, signal-to-noise ration (SNR) and total information capacity, and compare them with corresponding metrics that can be obtained with well-established MEG systems: based on superconducting quantum interference devices (SQUIDs) and optically-pumped magnetometers (OPMs). This simulation study is aimed to shed some light on the direction for further development of YIGM sensor, namely creation of multi-channel YIG-MEG.

neuroscience↗

bioRxiv2021

Evolution of MEG: a first MEG-feasible fluxgate magnetometer

In the current article we present a first solid-state sensor feasible for magnetoencephalography (MEG), and working at room temperature. The sensor is a fluxgate magnetometer based on yttrium-iron garnet films (YIGM). In this feasibility study we prove the concept of usage the YIGM in terms of MEG by registering a simple brain induced field: the human alpha rhythm 1. All the experiments and results are validated with usage of another kind of high-sensitive magnetometers - optically pumped magnetometer (OPM), which currently appears to be well-established in terms of MEG.

neuroscience↗

bioRxiv2020

Deep Learning for Cortical Potential Imaging

Electroencephalography (EEG) is a well-established non-invasive technique to measure the brain activity, albeit with a limited spatial resolution. Variations in electric conductivity between different tissues distort the electric fields generated by cortical sources, resulting in smeared potential measurements on the scalp. One needs to solve an ill-posed inverse problem to recover the original neural activity. In this article, we present a generic method of recovering the cortical potentials from the EEG measurement by introducing a new inverse-problem solver based on deep Convolutional Neural Networks (CNN) in paired (U-Net) and unpaired (DualGAN) configurations. The solvers were trained on synthetic EEG-ECoG pairs that were generated using a head conductivity model computed using the Finite Element Method (FEM). These solvers are the first of their kind, that provide robust translation of EEG data to the cortex surface using deep learning. Providing a fast and accurate interpretation of the tracked EEG signal, our approach promises a boost to the spatial resolution of the future EEG devices.

neuroscience↗