title: ÐÐ¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¾Ð½Ð½ÑÐ¹ Ð¿Ð¾Ð´Ñ Ð¾Ð´ Ð² Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ð¸ Ð¾Ð±Ð»Ð°ÑÑÐµÐ¹ Ð³Ð¸Ð¿Ð¿Ð¾ÐºÐ°Ð¼Ð¿Ð°
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Optimization approach to domain modelingÂ hippocampus
Creators: name: Ð¡ÐºÐ¾ÑÐ¾ÑÐ¾Ð´, Ð¡ÐµÑÐ³ÐµÐ¹
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: 2024
Subjects: Ð¼Ð°ÑÐµÐ¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ
Ð¸Ð¼Ð¿ÑÐ»ÑÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð³Ð¸Ð¿Ð¿Ð¾ÐºÐ°Ð¼Ð¿
mathematical modeling
optimization
spiking neural networks
hippocampus

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description: The thesis presents a model of a neural network segment of the brain with parameters optimized to best reproduce essential network effects. The networks connection structure closely resembles physiological patterns. Each neurons activity is described by a system of differential equations (DEs). The DEs are solved numerically using the Euler method and the exponential Euler method. The model is implemented in code using Python and Cython, leveraging computational graphs to enhance calculation speed. Unknown characteristics of neuronal activity are optimized to ensure the resulting activity of pyramidal cells closely aligns with experimental data. Parameters with reliable known values are not subject to optimization and are sourced from open literature. Optimization employs the differential evolution algorithm and the stochastic gradient descent algorithm with adaptive moments of the first and second orders. The parameters optimized include maximum synaptic conductances, firing frequencies at the center of the place field, the standard deviation and center relative to the center of the place field of the activity function of the listed neurons â totaling 38 parameters. The dynamics of the firing rate of CA1 pyramidal neurons will be evaluated. The network comprises populations of 10 different types â pyramidal neurons of hippocampal areas CA1, CA3, EC3, and inhibitory interneurons of types PVBAS, CCK, AAC, BIS, IVY, NGF, OLM in the CA1 area. Neuron types differ in many parameters, primarily the phase and frequency of firing relative to the theta rhythm, temporal dynamics relative to the stimulus, and connections â to the soma or dendrite of pyramidal or inhibitory neurons. The model of CA1 pyramidal neurons is a twocompartment (soma and dendrite) physiological model. Interneurons regulate the activity of pyramidal cells in the neocortex and hippocampus, playing a key role in many essential brain functions related to perception and cognition. As a result, a mathematical model of a neural ensemble was created, and optimal model parameters were obtained to reproduce fundamental network effects.