Compartmental spiking neuron model CSNM
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 hyperparameter...
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| Format: | Article |
| Language: | English |
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Saratov State University
2022-05-01
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| Series: | Известия высших учебных заведений: Прикладная нелинейная динамика |
| Subjects: | |
| Online Access: | https://andjournal.sgu.ru/sites/andjournal.sgu.ru/files/text-pdf/2022/05/bakhshiev-demcheva_299-310.pdf |
| _version_ | 1818251068567453696 |
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| author | Bakhshiev, Aleksandr Valeryevich Demcheva, Alexandra Andreevna |
| author_facet | Bakhshiev, Aleksandr Valeryevich Demcheva, Alexandra Andreevna |
| author_sort | Bakhshiev, Aleksandr Valeryevich |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T16:02:24Z |
| format | Article |
| id | doaj.art-ca89432c412a4245b277ecc21a84fc73 |
| institution | Directory Open Access Journal |
| issn | 0869-6632 2542-1905 |
| language | English |
| last_indexed | 2024-12-12T16:02:24Z |
| publishDate | 2022-05-01 |
| publisher | Saratov State University |
| record_format | Article |
| series | Известия высших учебных заведений: Прикладная нелинейная динамика |
| spelling | doaj.art-ca89432c412a4245b277ecc21a84fc732022-12-22T00:19:23ZengSaratov State UniversityИзвестия высших учебных заведений: Прикладная нелинейная динамика0869-66322542-19052022-05-0130329931010.18500/0869-6632-2022-30-3-299-310Compartmental spiking neuron model CSNMBakhshiev, Aleksandr Valeryevich0Demcheva, Alexandra Andreevna1Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya, 29Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya, 29The 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.https://andjournal.sgu.ru/sites/andjournal.sgu.ru/files/text-pdf/2022/05/bakhshiev-demcheva_299-310.pdfneuromorphic systemsspiking neural networkspiking neuroncompartment neuron model |
| spellingShingle | Bakhshiev, Aleksandr Valeryevich Demcheva, Alexandra Andreevna Compartmental spiking neuron model CSNM Известия высших учебных заведений: Прикладная нелинейная динамика neuromorphic systems spiking neural network spiking neuron compartment neuron model |
| title | Compartmental spiking neuron model CSNM |
| title_full | Compartmental spiking neuron model CSNM |
| title_fullStr | Compartmental spiking neuron model CSNM |
| title_full_unstemmed | Compartmental spiking neuron model CSNM |
| title_short | Compartmental spiking neuron model CSNM |
| title_sort | compartmental spiking neuron model csnm |
| topic | neuromorphic systems spiking neural network spiking neuron compartment neuron model |
| url | https://andjournal.sgu.ru/sites/andjournal.sgu.ru/files/text-pdf/2022/05/bakhshiev-demcheva_299-310.pdf |
| work_keys_str_mv | AT bakhshievaleksandrvaleryevich compartmentalspikingneuronmodelcsnm AT demchevaalexandraandreevna compartmentalspikingneuronmodelcsnm |