title: ÐÐ°ÑÐµÐ¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð°ÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð¾Ð², Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÑÑÑÐµÐ¹ ÑÐ´ÐµÑÐ¶Ð°Ð½Ð¸Ðµ ÐºÑÐ°ÑÐºÐ¾Ð³Ð¾ ÑÑÐ¸Ð¼ÑÐ»Ð° Ð·ÑÐ¸ÑÐµÐ»ÑÐ½Ð¾Ð¹ ÐºÐ¾ÑÐ¾Ð¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Mathematical modeling of neuronal activity, which determines the retention of a short stimulus by the visual cortex
Creators: name: ÐÐ¾Ð·ÐµÐ»ÐµÑÐºÐ°Ñ, ÐÐ°ÑÐ³Ð°ÑÐ¸ÑÐ°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2021
Subjects: ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÐÐ°ÑÐµÐ¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
Ð·ÑÐ¸ÑÐµÐ»ÑÐ½Ð°Ñ ÐºÐ¾ÑÐ°
Ð¼Ð°ÑÐµÐ¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
ÑÑÑÐµÐºÑ ÑÐ´ÐµÑÐ¶Ð°Ð½Ð¸Ñ ÑÑÐ¸Ð¼ÑÐ»Ð°
ÑÑÐ½ÐºÑÐ¸Ñ Ð²Ñ Ð¾Ð´Ð°-Ð²ÑÑ Ð¾Ð´Ð° Ð½ÐµÐ¹ÑÐ¾Ð½Ð°
NEURON

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¸Ð·ÑÑÐµÐ½Ð¸Ñ ÑÑÑÐµÐºÑÐ° ÑÐ´ÐµÑÐ¶Ð°Ð½Ð¸Ñ ÐºÑÐ°ÑÐºÐ¾Ð³Ð¾ ÑÑÐ¸Ð¼ÑÐ»Ð° Ð² Ð·ÑÐ¸ÑÐµÐ»ÑÐ½Ð¾Ð¹ ÐºÐ¾ÑÐµ Ð¸ Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½Ð¾Ð¹ ÑÑÐ½ÐºÑÐ¸Ð¸ Ð²ÑÐ¾Ð´Ð°-Ð²ÑÑÐ¾Ð´Ð° Ð½ÐµÐ¹ÑÐ¾Ð½Ð°. Ð ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð¼ÐµÑÐ¾Ð´Ð° Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð»Ð¾ÑÑ Ð¼Ð°ÑÐµÐ¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð² Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½ÑÑ ÑÑÐµÐ´Ð°Ñ Delphi Ð¸ NEURON+Python, ÑÐ¾Ð¾ÑÐ²ÐµÑÑÑÐ²ÐµÐ½Ð½Ð¾. Ð ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð±ÑÐ»Ð¸ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ñ Ð·Ð°Ð´Ð°ÑÐ¸: Ð¡Ð¾Ð·Ð´Ð°Ð½Ð¸Ðµ Ð´Ð²ÑÑ Ð¼Ð°ÑÐµÐ¼Ð°ÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ ÐºÐ¾Ð»ÑÑÐµÐ²ÑÑ ÑÐµÑÐµÐ¹ Ð¾ÑÐ¸ÐµÐ½ÑÐ°ÑÐ¸Ð¾Ð½Ð½ÑÑ Ð³Ð¸Ð¿ÐµÑÐºÐ¾Ð»Ð¾Ð½Ð¾Ðº Ð·ÑÐ¸ÑÐµÐ»ÑÐ½Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ñ Ð² Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð¹ ÑÑÐµÐ´Ðµ Delphi. ÐÐ½Ð°Ð»Ð¸Ð· ÑÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹, Ð¸Ð·ÑÑÐµÐ½Ð¸Ðµ ÑÐ°ÐºÑÐ¾ÑÐ¾Ð², Ð²Ð»Ð¸ÑÑÑÐ¸Ñ Ð½Ð° ÑÑÑÐµÐºÑ ÑÐ´ÐµÑÐ¶Ð°Ð½Ð¸Ñ. Ð¡Ð¾Ð·Ð´Ð°Ð½Ð¸Ðµ ÑÑÐµÑ Ð¼Ð¾Ð´ÐµÐ»ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð¾Ð² Ð² ÑÑÐµÐ´Ðµ NEURON+Python Ð¸ Ð¸Ñ Ð°Ð½Ð°Ð»Ð¸Ð· Ð² Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ðµ, Ð²ÑÐ¿Ð¾Ð»Ð½ÑÑÑÐµÐ¹ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ðµ ÑÐ°ÑÑÐ¾ÑÐ½Ð¾-ÑÐ¾ÐºÐ¾Ð²Ð¾Ð¹ ÑÐ°ÑÐ°ÐºÑÐµÑÐ¸ÑÑÐ¸ÐºÐ¸ Ð¸ Ð·Ð°Ð²Ð¸ÑÐ¸Ð¼Ð¾ÑÑÐ¸ ÑÐ°ÑÑÐ¾ÑÑ Ð½Ð° Ð¿Ð»Ð¾ÑÐºÐ¾ÑÑÐ¸ Ð¿ÑÐ¾Ð²Ð¾Ð´Ð¸Ð¼Ð¾ÑÑÑ/ÑÐ¾Ðº. ÐÐ¾ÐºÐ°Ð·Ð°Ð½Ð° Ð½ÐµÐ¾Ð±ÑÐ¾Ð´Ð¸Ð¼Ð¾ÑÑÑ ÑÐ¸Ð»ÑÐ½ÑÑ Ð²Ð¾Ð·Ð±ÑÐ¶Ð´Ð°ÑÑÐ¸Ñ ÑÐµÐºÑÑÑÐµÐ½ÑÐ½ÑÑ ÑÐ²ÑÐ·ÐµÐ¹ Ð´Ð»Ñ Ð½Ð°Ð±Ð»ÑÐ´ÐµÐ½Ð¸Ñ ÑÑÑÐµÐºÑÐ° ÑÐ´ÐµÑÐ¶Ð°Ð½Ð¸Ñ Ð¸ Ð½ÐµÐ¾Ð±ÑÐ¾Ð´Ð¸Ð¼Ð¾ÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ Ð´Ð²ÑÑÐ¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÑÐ½ÐºÑÐ¸Ð¸ Ð²ÑÐ¾Ð´Ð°-Ð²ÑÑÐ¾Ð´Ð° Ð½ÐµÐ¹ÑÐ¾Ð½Ð°.
description: This work is aimed at the study of the effect of retention of a short stimulus in the visual cortex and the optimal function of the input-output of a neuron. Mathematical modeling in Delphi and NEURON + Python software environments, respectively, was used as a method. During the study, the following tasks were solved: Creation of two mathematical models of circular networks of visual hypercolumn orientation in the Delphi software environment. Analysis of these models, study of the factors influencing the effect of retention. Creation of three model neurons in the NEURON + Python environment. Their analysis in a program that builds the frequency-current characteristic and frequency dependence on the conduction/current plane. The necessity of strong excitatory recurrent connections for observing the retention effect and the necessity of using a two-parameter function of the input-output of a neuron is shown.