ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)

For citation:

Bakhshiev A. V., Demcheva A. A. Compartmental spiking neuron model CSNM. Izvestiya VUZ. Applied Nonlinear Dynamics, 2022, vol. 30, iss. 3, pp. 299-310. DOI: 10.18500/0869-6632-2022-30-3-299-310

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Language: 
Russian
Heading: 
Nonlinear Dynamics and Neuroscience
Article type: 
Article
UDC: 
004.032.26+004.274
DOI: 
10.18500/0869-6632-2022-30-3-299-310

Compartmental spiking neuron model CSNM

Autors: 
Bakhshiev Aleksandr Valeryevich, Peter the Great St. Petersburg Polytechnic University
Demcheva Alexandra Andreevna, Peter the Great St. Petersburg Polytechnic University
Abstract: 

The purpose of this work is to develop a compartment spiking neuron model as an element of growing neural networks. Methods. As part of the work, the CSNM is compared with the Leaky Integrate-and-Fire model by comparing the reactions of point models to a single spike. The influence of hyperparameters of the proposed model on neuron excitation is also investigated. All the described experiments were carried out in the Simulink environment using the tools of the proposed library. Results. It was concluded that the proposed model is able to qualitatively reproduce the reaction of the point classical model, and the tuning of hyperparameters allows reproducing the following patterns of signal propagation in a biological neuron: a decrease in the maximum potential and an increase in the delay between input and output spikes with an increase in the size of the neuron or the length of the dendrite, as well as an increase in the potential with an increase in the number of active synapses. Conclusion. The proposed compartment spiking neuron model allows to describe the behavior of biological neurons at the level of pulse signal conversion. The hyperparameters of the model allow tuning the neuron responses at fixed other neuron parameters. The model can be used as a part of spiking neural networks with details at the level of compartments of neurons dendritic trees.

Key words: 
neuromorphic systems
spiking neural network
spiking neuron
compartment neuron model
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Received: 
07.11.2021
Accepted: 
20.12.2021
Published: 
31.05.2022
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