Symbiosis of an artificial neural network and models of biological neurons: 
Training and testing

Bogatenko, Tatyana; Sergeev, Konstantin; Slepnev, Andrei; Kurths, Jürgen; Nadezhda Semenova, Nadezhda

doi:10.1063/5.0152703

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Journal Article

Symbiosis of an artificial neural network and models of biological neurons: Training and testing

Authors

Bogatenko, Tatyana
External Organizations;

Sergeev, Konstantin
External Organizations;

Slepnev, Andrei
External Organizations;

/persons/resource/Juergen.Kurths

Kurths, Jürgen
Potsdam Institute for Climate Impact Research;

Nadezhda Semenova, Nadezhda
External Organizations;

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

28951oa.pdf
(Publisher version), 2MB

Supplementary Material (public)

There is no public supplementary material available

Citation

Bogatenko, T., Sergeev, K., Slepnev, A., Kurths, J., Nadezhda Semenova, N. (2023): Symbiosis of an artificial neural network and models of biological neurons: Training and testing. - Chaos, 33, 7, 073122.
https://doi.org/10.1063/5.0152703

Cite as: https://publications.pik-potsdam.de/pubman/item/item_28951

Abstract

In this paper, we show the possibility of creating and identifying the features of an artificial neural network (ANN), which consists of mathematical models of biological neurons. The FitzHugh–Nagumo (FHN) system is used as a paradigmatic model demonstrating basic neuron activities. First, in order to reveal how biological neurons can be embedded within an ANN, we train the ANN with nonlinear neurons to solve a basic image recognition problem with an MNIST database; next, we describe how FHN systems can be introduced into this trained ANN. After all, we show that an ANN with FHN systems inside can be successfully trained with improved accuracy comparing with first trained ANN and then with inserted FHN systems. This approach opens up great opportunities in terms of the direction of analog neural networks, in which artificial neurons can be replaced by more appropriate biological ones.