Instrumental implementation of a neuronlike generator with spiking and bursting dynamics based on a phase-locked loop

Mishchenko, Mikhail; Bolshakov, Denis I.; Matrosov, V. V.

doi:10.1134/s1063785017070100

Cited by 13 publications

(17 citation statements)

References 23 publications

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“…The studied here experimental generator with bandpass filter constructed in [20] uses logical XOR element as a phase discriminator. The bandpass filter transfer function is described by the formula…”

Section: Arxiv:210209638v1 [Eesssy] 17 Feb 2021mentioning

confidence: 99%

“…Here, we consider the PLL system with bandpass filter [16] which is interesting for multiple application due to many different regimes it provides [17], [18] and can be considered as a model of both radiophysical circuits and biological neurons, especially after some additional assumptions [19]. In this work we aim to reconstruct the proposed in [16] model from experimental time series of the generator constructed in [20] and detect qualitatively and quantitatively how much a typical PLL mathematical model really describes the experimental setup.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Phase-Locked Loop System From Its Experimental Time Series

Mishchenko

Bolshakov

Vasin

et al. 2022

IEEE Trans. Circuits Syst. II

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Phase-locked loops (PLLs) are now widely used in communication systems and have been a classic system for more than 60 years. Well-known mathematical models of such systems are constructed in a number of approximations, so questions about how they describe the experimental dynamics qualitatively and quantitatively, and how the accuracy of the model description depends on the behavior mode, remain open. One of the most direct approaches to the verification of any model is its reconstruction from the time series obtained in experiment. If it is possible to fit the model to experimental data and the resulting parameter values are close to the expected values (calculated from the first principles), the quantitative correspondence between the model and the physical object is nearly proved. In this paper, for the first time, the equations of the PLL model with a bandpass filter are reconstructed from the experimental signals of the generator in various modes. The reconstruction showed that the model known in the literature generally describes the experimental dynamics in regular and chaotic regimes. The relative error of parameter estimation is between 2% and 50% for different regimes and parameters. The reconstructed nonlinear function of phase is not harmonic and highly asymmetric in contrary to the model one.

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Section: Arxiv:210209638v1 [Eesssy] 17 Feb 2021mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of Phase-Locked Loop System From Its Experimental Time Series

Mishchenko

Bolshakov

Vasin

et al. 2022

IEEE Trans. Circuits Syst. II

View full text Add to dashboard Cite

show abstract

“…The history of neuromorphic technologies began in the late 1980s with the emergence of computation machines, and since then, significant advances have been achieved in elec-Sensors 2021, 21, 5587 2 of 12 tronics, physics of micro-and nanostructures, and solid-state nanoelectronics. The careful development of neuron-like electrical circuits made it possible to reproduce basic neural behaviors, such as resting, spiking, and bursting dynamics, as well as more sophisticated regimes, including chaos and multistability [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Memristive Interface for Neural Signal Processing

Gerasimova

Belov

Королев

et al. 2021

Sensors

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We propose a memristive interface consisting of two FitzHugh–Nagumo electronic neurons connected via a metal–oxide (Au/Zr/ZrO2(Y)/TiN/Ti) memristive synaptic device. We create a hardware–software complex based on a commercial data acquisition system, which records a signal generated by a presynaptic electronic neuron and transmits it to a postsynaptic neuron through the memristive device. We demonstrate, numerically and experimentally, complex dynamics, including chaos and different types of neural synchronization. The main advantages of our system over similar devices are its simplicity and real-time performance. A change in the amplitude of the presynaptic neurogenerator leads to the potentiation of the memristive device due to the self-tuning of its parameters. This provides an adaptive modulation of the postsynaptic neuron output. The developed memristive interface, due to its stochastic nature, simulates a real synaptic connection, which is very promising for neuroprosthetic applications.

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“…In the last decades, different electronic circuits have been designed to generate neural dynamics. They are distinguished by their biological relevance, a number of simulated ion channels and a number of represented dynamic modes [ 11 – 18 ]. Collective dynamics of artificial neurons were also extensively studied [ 19 – 22 ] with concentrated efforts in modelling and implementing synaptic connections in hardware.…”

Section: Introductionmentioning

confidence: 99%

Optoelectronic system for brain neuronal network stimulation

et al. 2018

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We propose an optoelectronic system for stimulation of living neurons. The system consists of an electronic circuit based on the FitzHugh–Nagumo model, an optical fiber, and a photoelectrical converter. We used this system for electrical stimulation of hippocampal living neurons in acute hippocampal brain slices (350-μm thick) obtained from a 20–28 days old C57BL/6 mouse or a Wistar rat. The main advantage of our system over other similar stimulators is that it contains an optical fiber for signal transmission instead of metallic wires. The fiber is placed between the electronic circuit and stimulated neurons and provides galvanic isolation from external electrical and magnetic fields. The use of the optical fiber allows avoiding electromagnetic noise and current flows which could affect metallic wires. Furthermore, it gives us the possibility to simulate “synaptic plasticity” by adaptive signal transfer through optical fiber. The proposed optoelectronic system (hybrid neural circuit) provides a very high efficiency in stimulating hippocampus neurons and can be used for restoring brain activity in particular regions or replacing brain parts (neuroprosthetics) damaged due to a trauma or neurodegenerative diseases.

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Instrumental implementation of a neuronlike generator with spiking and bursting dynamics based on a phase-locked loop

Cited by 13 publications

References 23 publications

Identification of Phase-Locked Loop System From Its Experimental Time Series

Identification of Phase-Locked Loop System From Its Experimental Time Series

Stochastic Memristive Interface for Neural Signal Processing

Optoelectronic system for brain neuronal network stimulation

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