arXiv subjects

A. K. Vidybida

Publications and source records attributed to A. K. Vidybida.

4 recordsLinked to original sources

arXiv2025

Selectivity gain in olfactory receptor neuron at optimal odor concentration

It has been discovered before (arXiv:2306.07676) that for the selectivity gain due to fluctuations in the process of primary odor reception by olfactory receptor neuron (ORN) there exists an optimal concentration of odors at which increased selectivity is mostly manifested. We estimate by means of numerical simulation what could be the gain value at that concentration by modeling ORN as a leaky integrate-and-fire neuron with membrane populated by receptor proteins R which bind and release odor molecules randomly. Each R is modeled as a ligand-gated ion channel, and binding-releasing is modeled as a Markov stochastic process. Possible values for the selectivity gain are calculated for ORN parameters suggested by experimental data. Keywords: ORN, selectivity, receptor proteins, fluctuations, stochastic process, Markov process

q-bio.NC↗

arXiv2015

Output stream of leaky integrate and fire neuron

Probability density function of output interspike intervals is found in exact form for leaky integrate and fire neuron stimulated with Poisson stream. The diffusion approximation is not exploited.

q-bio.NC↗

arXiv2015

Simulating leaky integrate and fire neuron with integers

The leaky integrate and fire (LIF) neuron represents standard neuronal model used for numerical simulations. The leakage is implemented in the model as exponential decay of trans-membrane voltage towards its resting value. This makes inevitable the usage of machine floating point numbers in the course of simulation. It is known that machine floating point arithmetic is subjected to small inaccuracies, which prevent from exact comparison of floating point quantities. In particular, it is incorrect to decide whether two separate in time states of a simulated system composed of LIF neurons are exactly identical. However, decision of this type is necessary, e.g. to figure periodic dynamical regimes in a reverberating network. Here we offer a simulation paradigm of a LIF neuron, in which neuronal states are described by whole numbers. Within this paradigm, the LIF neuron behaves exactly the same way as does the standard floating point simulated LIF, although exact comparison of states becomes correctly defined.

q-bio.NC↗

arXiv2007

Information processing at single neuron level

Based on numerical simulation of Hodgkin and Huxley type neuron stimulated from many synaptic inputs, an abstract concept of signal processing in individual neuron is proposed. In the concept proposed, neuron performs binding of synaptic inputs into a single output event, based on the degree of temporal coherence between the inputs. Inhibition serves as controlling factor of this type of binding.

q-bio.NC↗