title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð°Ð¿Ð¿Ð°ÑÐ°ÑÐ½Ð¾Ð¹ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ ÑÐ¿Ð°Ð¹ÐºÐ¾Ð²ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð¾Ð² Ð½Ð° Ð°Ð½Ð°Ð»Ð¾Ð³Ð¾Ð²Ð¾Ð¹ ÑÐ»ÐµÐ¼ÐµÐ½ÑÐ½Ð¾Ð¹ Ð±Ð°Ð·Ðµ
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: Hardware implementation of the mathematic spike model of the CSNM neuron
Creators: name: ÐÐ¾Ð¹ÐºÐ¾, ÐÐ»ÐµÐºÑÐ°Ð½Ð´Ñ
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: ÑÐ¿Ð°Ð¹ÐºÐ¾Ð²ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð½ÐµÐ¹ÑÐ¾Ð¼Ð¾ÑÑÐ½ÑÐµ ÑÐ¸ÑÑÐµÐ¼Ñ
ÑÐ¿Ð°Ð¹ÐºÐ¾Ð²ÑÐ¹ Ð½ÐµÐ¹ÑÐ¾Ð½
ÑÐµÐ³Ð¼ÐµÐ½ÑÐ½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð°
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¾Ð¿ÐµÑÐ°ÑÐ¸Ð¾Ð½Ð½ÑÐ¹ ÑÑÐ¸Ð»Ð¸ÑÐµÐ»Ñ
Ð°Ð¿Ð¿Ð°ÑÐ°ÑÐ½Ð°Ñ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ
spike neural networks
deep learning
neuromorphic systems
spike neuron
compartmental neuron model
machine learning
operational amplifier
hardware implementation

description: ÐÐ±ÑÐµÐºÑÐ¾Ð¼ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð² ÑÐ°Ð¼ÐºÐ°Ñ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐ»Ð°ÑÑ Ð°Ð¿Ð¿Ð°ÑÐ°ÑÐ½Ð°Ñ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð¼Ð°ÑÐµÐ¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¸Ð¼Ð¿ÑÐ»ÑÑÐ½Ð¾Ð³Ð¾ Ð½ÐµÐ¹ÑÐ¾Ð½Ð° CSNM. Ð¦ÐµÐ»ÑÑ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð°Ð¿Ð¿Ð°ÑÐ°ÑÐ½Ð¾Ð¹ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸, ÐºÐ¾ÑÐ¾ÑÐ°Ñ Ð±Ñ ÐºÐ°ÑÐµÑÑÐ²ÐµÐ½Ð½Ð¾ Ð¿Ð¾Ð²ÑÐ¾ÑÑÐ»Ð° Ð¿Ð¾Ð²ÐµÐ´ÐµÐ½Ð¸Ðµ Ð¼Ð°ÑÐµÐ¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸. Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð±ÑÐ»Ð° Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð° Ð² ÑÐ¸Ð¼ÑÐ»ÑÑÐ¸Ð¸ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¿Ð°ÐºÐµÑÐ° Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ»ÐµÐºÑÑÐ¾ÑÐµÐ¿ÐµÐ¹ LTSpice. ÐÑÐ» Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð¾Ð² Ðº ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð¾Ð². ÐÑÐ»Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð° ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð´Ð¸ÑÑÐµÑÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½ÑÑ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ð¹ Ð´Ð»Ñ ÐºÐ°Ð¶Ð´Ð¾Ð³Ð¾ ÑÐ»ÐµÐ¼ÐµÐ½ÑÐ° Ð¼Ð¾Ð´ÐµÐ»Ð¸, Ð¿Ð¾ÑÐ»Ðµ ÑÐµÐ³Ð¾ Ð±ÑÐ»Ð¸ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ñ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÑ Ñ Ð¾ÑÐ´ÐµÐ»ÑÐ½ÑÐ¼Ð¸ ÑÐ»ÐµÐ¼ÐµÐ½ÑÐ°Ð¼Ð¸ Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð¾Ð¼ Ð² ÑÐµÐ»Ð¾Ð¼. Ð ÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð° ÑÐµÐ±Ñ Ð´Ð¾ÑÑÐ°ÑÐ¾ÑÐ½Ð¾ ÑÐ¾ÑÐ¾ÑÐ¾ Ð¸ Ð¿Ð¾Ð²ÑÐ¾ÑÐ¸Ð»Ð° Ð¿Ð¾Ð²ÐµÐ´ÐµÐ½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸. ÐÑÐ¾Ð±ÐµÐ½Ð½Ð¾ÑÑÑÑ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸, Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð½Ð¾Ð¹ Ð² Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ, ÑÐ²Ð»ÑÐµÑÑÑ ÐµÐµ Ð¼Ð¾Ð´ÑÐ»ÑÐ½Ð¾ÑÑÑ Ñ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÑÑ ÑÐ¾Ð±Ð¸ÑÐ°ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ñ ÑÐ°Ð·Ð»Ð¸ÑÐ½Ð¾Ð¹ ÐºÐ¾Ð½ÑÐ¸Ð³ÑÑÐ°ÑÐ¸Ð¸. ÐÐ¸ÑÐ°Ð½Ð¸Ðµ ÑÐ¸ÑÑÐµÐ¼Ñ Ð¸ ÐµÐµ Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¾Ðµ Ð½Ð°Ð¿ÑÑÐ¶ÐµÐ½Ð¸Ðµ Ð¾Ð´Ð¸Ð½Ð°ÐºÐ¾Ð²Ñ Ð¸ ÑÐ¾ÑÑÐ°Ð²Ð»ÑÑÑ 5 Ð²Ð¾Ð»ÑÑ. ÐÐ° Ð²ÑÐ¾Ð´Ðµ Ð¸ Ð²ÑÑÐ¾Ð´Ðµ ÑÐ¸ÑÑÐµÐ¼Ñ â Ð¿Ð¾ÑÐ»ÐµÐ´Ð¾Ð²Ð°ÑÐµÐ»ÑÐ½Ð¾ÑÑÑ Ð¸Ð¼Ð¿ÑÐ»ÑÑÐ¾Ð² Ð´Ð»Ð¸Ð½Ð¾Ð¹ 1 Ð¼Ñ. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð±ÑÐ´ÐµÑ Ð¿Ð¾Ð»ÐµÐ·Ð½Ð° Ð´Ð»Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ Ð½Ð° ÑÐ¾Ð±Ð¾ÑÐ°Ñ Ð¸Ð»Ð¸ Ð´ÑÑÐ³Ð¸Ñ ÑÑÑÐ°Ð½Ð¾Ð²ÐºÐ°Ñ, Ð³Ð´Ðµ Ð½ÑÐ¶Ð½Ð° Ð¸Ð¼Ð¿ÑÐ»ÑÑÐ½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ, Ð½Ð¾ Ð·Ð°ÑÑÐ°ÑÐ½Ð¾ Ð¸Ð»Ð¸ Ð½ÐµÑÐ´Ð¾Ð±Ð½Ð¾ Ð¿Ð¾Ð´ÐºÐ»ÑÑÐ°ÑÑ ÑÐ¸ÑÑÐµÐ¼Ñ Ñ Ð¼Ð¾ÑÐ½ÑÐ¼Ð¸ Ð²ÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½ÑÐ¼Ð¸ ÑÐ¿Ð¾ÑÐ¾Ð±Ð½Ð¾ÑÑÑÐ¼Ð¸ Ð´Ð»Ñ ÑÐ¸Ð¼ÑÐ»ÑÑÐ¸Ð¸ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐ¸ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ ÐºÐ¾Ð´Ð°. Ð Ð´Ð°Ð»ÑÐ½ÐµÐ¹ÑÐµÐ¼ Ð¿Ð»Ð°Ð½Ð¸ÑÑÐµÑÑÑ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ ÑÐ½ÐµÑÐ³Ð¾Ð¿Ð¾ÑÑÐµÐ±Ð»ÐµÐ½Ð¸Ñ ÑÑÐµÐ¼Ñ, ÑÐ±Ð¾ÑÐºÐ° Ð¿ÑÐ¾ÑÐ¾ÑÐ¸Ð¿Ð° Ð¸ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ðµ Ð¸ÑÐ¿ÑÑÐ°Ð½Ð¸Ð¹ Ñ Ð½Ð¸Ð¼.
description: In this work the object of development was hardware implementation of the mathematic spike model of the CSNM neuron. This workâs goal was to develop hardware implementation that would reliably copy modelâs behavior. In this work the implementation was made in simulation using modelling package LTSpice for electric circuits. An analysis of existing approaches to implementing neurons was conducted. Implementation of differential equations for each model element was developed, followed by experiments with individual elements and the neuron in assembly. The implementation performed well and replicated the models behavior. A feature of the implementation presented in this work is its modularity with the ability of assembling neurons into different configurations. The systems power supply and logical voltage are the same and amount to 5 volts. Input and output of the system have the form of a sequence of 1 ms impulses. The development will be good for use in robots or other situations where a spike neural network is needed but connecting a system with high computational power for simulating a neural network through code is costly or inconvenient. Energy efficiency was not considered in this work, so it is included in plans. Additionally, prototyping, and further testing are planned.