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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Experimental Validation and Reservoir Computing Capability of Spiking Neuron Based on Threshold Selector and Tunnel Diode

by
Vasiliy Pchelko
1,
Vladislav Kholkin
1,
Vyacheslav Rybin
1,2,*,
Alexander Mikhailov
1 and
Timur Karimov
1,2
1
Department of Computer-Aided Design, Saint Petersburg Electrotechnical University ‘‘LETI’’, Professora Popova St. 5F, Saint Petersburg 197022, Russia
2
Youth Research Institute, Saint Petersburg Electrotechnical University ‘‘LETI’’, Professora Popova St. 5F, Saint Petersburg 197022, Russia
*
Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2026, 10(4), 115; https://doi.org/10.3390/bdcc10040115
Submission received: 22 February 2026 / Revised: 27 March 2026 / Accepted: 8 April 2026 / Published: 10 April 2026
Versions Notes

Abstract

Despite the success of artificial neural networks in solving numerous tasks, they face significant challenges, including difficulties in online adaptation and rapidly increasing energy consumption. As a biologically plausible alternative, spiking neural networks offer promising capabilities for efficient cognitive computing. Recently, a three-element spiking neuron model consisting of a threshold selector, a tunnel diode, and a capacitor was proposed. In this work, we experimentally validate this model using a threshold selector hardware emulator and demonstrate its dynamical equivalence to the biologically plausible Izhikevich neuron model. To evaluate the novel neuron’s applicability for cognitive computing, we implement a liquid state machine (LSM) reservoir architecture with spatially dependent random topology for synaptic weight distribution. Our simulations on the MNIST and Fashion-MNIST benchmarks demonstrate competitive classification accuracy (97.9% and 89.5%, respectively) while offering estimated energy efficiency and processing speed enhancements compared to existing FPGA-based and memristor-based spiking reservoir implementations. The developed reservoir is feasible for processing neuromorphic sensors output, including visual perception tasks.
Keywords: reservoir computer; liquid state machine; neuromorphic computing; MNIST benchmark; analog neuron model; spiking neuron reservoir computer; liquid state machine; neuromorphic computing; MNIST benchmark; analog neuron model; spiking neuron

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MDPI and ACS Style

Pchelko, V.; Kholkin, V.; Rybin, V.; Mikhailov, A.; Karimov, T. Experimental Validation and Reservoir Computing Capability of Spiking Neuron Based on Threshold Selector and Tunnel Diode. Big Data Cogn. Comput. 2026, 10, 115. https://doi.org/10.3390/bdcc10040115

AMA Style

Pchelko V, Kholkin V, Rybin V, Mikhailov A, Karimov T. Experimental Validation and Reservoir Computing Capability of Spiking Neuron Based on Threshold Selector and Tunnel Diode. Big Data and Cognitive Computing. 2026; 10(4):115. https://doi.org/10.3390/bdcc10040115

Chicago/Turabian Style

Pchelko, Vasiliy, Vladislav Kholkin, Vyacheslav Rybin, Alexander Mikhailov, and Timur Karimov. 2026. "Experimental Validation and Reservoir Computing Capability of Spiking Neuron Based on Threshold Selector and Tunnel Diode" Big Data and Cognitive Computing 10, no. 4: 115. https://doi.org/10.3390/bdcc10040115

APA Style

Pchelko, V., Kholkin, V., Rybin, V., Mikhailov, A., & Karimov, T. (2026). Experimental Validation and Reservoir Computing Capability of Spiking Neuron Based on Threshold Selector and Tunnel Diode. Big Data and Cognitive Computing, 10(4), 115. https://doi.org/10.3390/bdcc10040115

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