Study of the influence of synaptic plasticity on the formation of a feature space by a spiking neural network
The purpose of this study is to study the influence of synaptic plasticity on excitatory and inhibitory synapses on the formation of the feature space of the input image on the excitatory and inhibitory layers of neurons in a spiking neural network. Methods. To simulate the dynamics of the neuron,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Saratov State University
2024-03-01
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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/2024/03/and_2024-2_lebedev-et-al_253-267.pdf |
| Summary: | The purpose of this study is to study the influence of synaptic plasticity on excitatory and inhibitory synapses on the formation of the feature space of the input image on the excitatory and inhibitory layers of neurons in a spiking neural network. Methods. To simulate the dynamics of the neuron, the computationally efficient model “Leaky integrate-and-fire” was used. The conductance-based synapse model was used as a synaptic contact model. Synaptic plasticity in excitatory and inhibitory synapses was modeled by the classical model of time dependent synaptic plasticity. A neural network composed of them generates a feature space, which is divided into classes by a machine learning algorithm. Results. A model of a spiking neural network was built with excitatory and inhibitory layers of neurons with adaptation of synaptic contacts due to synaptic plasticity. Various configurations of the model with synaptic plasticity were considered for the problem of forming the feature space of the input image on the excitatory and inhibitory layers of neurons, and their comparison was also carried out. Conclusion. It has been shown that synaptic plasticity in inhibitory synapses impairs the formation of an image feature space for a classification task. The model constraints are also obtained and the best model configuration is selected. |
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| ISSN: | 0869-6632 2542-1905 |