Papers referencing ArNI-X
ArNI-X has been used in several research projects conducted in Chuvash State University, RosAtom state corporation, Kaspersky and others.
These are some papers describing these studies.
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Kiselev M., Ivanitsky A., Ivanov D., Larionov D. (2023)
A Spiking Neuron Synaptic Plasticity Model Optimized for Unsupervised Learning
NEUROINFORMATICS 2023 , Studies in Computational Intelligence, vol. 1120. Springer, pp 111-120.
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Kiselev M., Lavrentyev A., Larionov D., Ivanov D. (2024)
From "What" to "When" -a Spiking Neural Network Predicting Rare Events and Time to their Occurrence
Accepted to IJCNN-24
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Kiselev M., Ivanitsky A., and Lavrentyev A. (2023)
A Spiking Neural Network Learning Markov Chain
IJCNN-23 paper 1570879403
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Kiselev M., and Lavrentyev A. (2023)
“GAS” Instead of “Liquid”: Which Liquid State Machine is Better?
NEUROINFORMATICS 2022 Studies in Computational Intelligence, vol. 1064. Springer, pp 479-489
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Kiselev M., Ivanitsky A., and Lavrentyev A. (2022)
Comparison of Memory Mechanisms Based on Adaptive Threshold Potential and Short-Term Synaptic Plasticity.
NEUROINFORMATICS 2021 Studies in Computational Intelligence, vol. 1008. Springer, pp 334-343.
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Kiselev M., Ivanov A., and Ivanov D. (2021)
Approximating Conductance-Based Synapses by Current-Based Synapses
NEUROINFORMATICS 2020 Studies in Computational Intelligence, vol 925. Springer, pp 394-402.
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Kiselev M. (2020)
Chaotic Spiking Neural Network Connectivity Configuration Leading to Memory Mechanism Formation.
NEUROINFORMATICS 2019 Studies in Computational Intelligence, vol 856. Springer, Cham pp 398-404.
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Kiselev M., Lavrentyev A. (2019)
A Preprocessing Layer in Spiking Neural Networks – Structure, Parameters, Performance Criteria
IJCNN-2019 paper N-19450 -
Kiselev M. (2017)
A Synaptic Plasticity Rule Providing a Unified Approach to Supervised and Unsupervised Learning
IJCNN-2017 pp 3806-3813 -
Kiselev M. (2016)
Rate Coding vs. Temporal Coding – Is Optimum Between?
IJCNN-2016 pp 1355-1359