arXiv subjects

A. Vidybida

Publications and source records attributed to A. Vidybida.

3 recordsLinked to original sources

arXiv2022

Harnessing thermal fluctuations for selectivity gain

Selectivity of olfactory receptor neuron (ORN) is compared with that of its receptor proteins (R) with fluctuations of odor binding-releasing process taken into account. The binding-releasing process is modeled as N Bernoulli trials, where N is the total number of R per ORN. Dimensionless selectivities for both R and ORN are introduced and compared with each other. It is found the ORN's selectivity can be much higher then that of its receptor proteins. This effect is concentration-dependent. Possible application for biosensors is discussed. URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9789678&isnumber=9789552

physics.bio-ph↗

arXiv2022

From chaos to clock in reverberating neural net. Case study

What is the reason for complex dynamical patterns registered from real biological neuronal networks? Noise and dynamical reconfiguring of a network (functional/dynamic connectome) were proposed as possible answers. In this case study, we report a complex dynamical pattern observed in a simple deterministic network of 25 neurons with fixed connectome. After a short initial stimulation, the network is engaged into a complex dynamics, which lasts for a long time. Eventually, with no external intervention, the dynamics comes to a periodic one with a short period. The long transient is positively checked for being chaotic. We conclude that the complex dynamics observed is the output of neural computation performed in the process of neuronal firings and spikes propagation.

q-bio.NC↗

arXiv2017

Information reduction in a reverberatory neuronal network through convergence to complex oscillatory firing patterns

We study dynamics of a reverberating neural net by means of computer simulation. The net, which is composed of 9 leaky integrate-and-fire (LIF) neurons arranged in a square lattice, is fully connected with interneuronal communication delay proportional to the corresponding distance. The network is initially stimulated with different stimuli and then goes freely. For each stimulus, in the course of free evolution, activity either dies out completely or the network converges to a periodic trajectory, which may be different for different stimuli. The latter is observed for a set of 285290 initial stimuli which constitutes 83% of all stimuli applied. By applying each stimulus from the set, we found 102 different periodic end-states. By analyzing the trajectories, we conclude that neuronal firing is the necessary prerequisite for merging different trajectories into a single one, which eventually transforms into a periodic regime. Observed phenomena of self-organization in the time domain are discussed as a possible model for processes taking place during perception. The repetitive firing in the periodic regimes could underpin memory formation.

q-bio.NC↗