Sinir hücrelerinin ve bağlantılarının donanım üzerinde gerçeklenmesi
Implementing neuron models and their connections on a hardware
Yazar: HASAN ÖZDEMİRCİ
Danışman: PROF. DR. NESLİHAN SERAP ŞENGÖR
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / Elektronik ve Haberleşme Mühendisliği Ana Bilim Dalı / Elektronik Mühendisliği Bilim Dalı
Derece: Yüksek Lisans, 2021
Konu: Elektrik ve Elektronik Mühendisliği = Electrical and Electronics Engineering
Dizin: Beyin hücreleri = Brain cells ; FPGA = FPGA

Özet:
Bu çalışmada Izhikevich'in önerdiği sinir hücresi modelinin matematiksel ifadesindeki farklı parametrelere göre davranışındaki değişimin incelenmesi için, önce MATLAB Simulink'de ve daha sonra da Xilinx xblokları kullanılarak Simulink üzerinden Field Programmable Gate Array (FPGA) uygun olarak modellenmiştir.
SIMULINK üzerinde, basit bir nöral ağ gerçeklenmiştir. 20 tane uyarıcı hücre ve 4 tane bastırıcı hücreden oluşan nöral ağda, 20 tane uyarıcı hücre Poisson dağılımı ile uyarılmakta, 4 bastırıcı hücrede bu uyarıcı hücreleri bastırmaktadır. Nöral ağın çıkışı olarak ateşleme grafiklerine bakılmaktadır. 
Çalışmanın ikinci bölümünde tek bir sinir hücresinin FPGA ortamında çalışmasını incelemek için xblocklar kullanarak modellemesi yapılmıştır.Model bir differansiyel denklem takımından olu¸stu˘gu için önce bir Rieman toplamı yöntemine göre çalışan bir integral alıcı kullanılmış ve değisik işaretler verilerek doğruluğu test edilmiştir. Bu integral alıcı kullanılarak sinir hücresine ilişkin matematiksel model xbloklarla simulink ortamında çözümü sağlayacak biçimde gerçekleştirilmiştir.

Abstract:
In this work, considering a mathematical model of neuron and Hebbian rule a small network composed of spiking neurons is realized on software and hardware. As mathematical neuron model Izhikevich model is considered and for realizing learning dynamical synapses are considered. Hebbian learning which is proposed by Donald Hebb for the interaction between two neurons is a simple and efficient way of modelling learning at cell level, thus synaptic dynamics are changed according to Hebbian rule.
In this thesis, MATLAB SIMULINK is considered as software environment and then FPGA (Field Programmable Gate Array) is considered as hardware environment. As the main idea is to use this realization in building a neuronal structure, SIMULINK is used since a large network of neurons could be best realized using a modular approach.
Once a structure is built in a compact way to model a neuron, this structure can be used repeatedly to realize a network.
In the first section of the thesis, the problem is explained and a literature survey is given. In the section 2, Izhikevich neuron model is introduced and its implementation with conventional SIMULINK blocks is given. Also model of synapse as a dynamical system is given.A simple cortex model,composed of excitatory, inhibitory neurons and synaptic connections is also realizedwith SIMULINK blocks.
As a first step, a single neuron model is implemented in MATLAB SIMULINK environment and then model consisting of two neurons is modeled with a dynamic interconnection as synapse.In the next step, a three-layer multi-neuron model is designed using two-cell firing model. The first layer of the multi-neuron model consists of the stimulating neurons, the neurons in the second layer are stimulated by the first layer and the third layer is
composed of inhibitory neurons. The firing of the first layer neurons stimulates the second layer cells through dynamic connections. While the first layer stimulates the second layer, the third layer inhibits the neurons in the second layer.
In section 4, how to use Xlinix Virtex-6 FPGA in realizing a single Izhikevih neuron model is explained. Using the models made in MATLAB SIMULINK, the one neuron firing model is implemented on the FPGA hardware environment. In order to examine one neuron firing in an FPGA environment, it is first modeled using Xilinx blokes.In this modeling, Xilinx blokes are realized in Simulink environment first. Since the mathematical model consists of a set of differential equations, an integrator working according to a Rieman sum method is used first and its accuracy is tested by giving different signs. By using this integrator, the mathematical model of the neuron is realized with Xilinx blokes to provide the solution obtained in the SIMULINK environment.