Effect of Transcranial Ultrasonic–Magnetic Stimulation on Two Types of Neural Firing Behaviors in Modified Izhikevich Model

Zhang, Shuai; Cui, Kun; Shi, Xun; Ge, Manling; Zhao, M. G.; Xu, Guizhi; Yan, Weili

doi:10.1109/tmag.2017.2773086

Cited by 13 publications

(8 citation statements)

References 14 publications

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“…The Izhikevich neuron model incorporates the CORDIC algorithms to implement a neuromorphic system with high speed and accuracy (Heidarpur et al, 2019 ). The modified IZH model with mechanoelectrical and ultrasonic-magnetic effects can produce distinct spiking behaviors (Zhang et al, 2018 ). The bionic tactile sensor outputs are applied to the IZH model to simulate the spiking patterns and achieve the artificial touch (Rongala et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Memristive Izhikevich Spiking Neuron Model and Its Application in Oscillatory Associative Memory

2022

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The Izhikevich (IZH) spiking neuron model can display spiking and bursting behaviors of neurons. Based on the switching property and bio-plausibility of the memristor, the memristive Izhikevich (MIZH) spiking neuron model is built. Firstly, the MIZH spiking model is introduced and used to generate 23 spiking patterns. We compare the 23 spiking patterns produced by the IZH and MIZH spiking models. Secondly, the MIZH spiking model actively reproduces various neuronal behaviors, including the excitatory cortical neurons, the inhibitory cortical neurons, and other cortical neurons. Finally, the collective dynamic activities of the MIZH neuronal network are performed, and the MIZH oscillatory network is constructed. Experimental results illustrate that the constructed MIZH spiking neuron model performs high firing frequency and good frequency adaptation. The model can easily simulate various spiking and bursting patterns of distinct neurons in the brain. The MIZH neuronal network realizes the synchronous and asynchronous collective behaviors. The MIZH oscillatory network can memorize and retrieve the information patterns correctly and efficiently with high retrieval accuracy.

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Section: Introductionmentioning

confidence: 99%

Memristive Izhikevich Spiking Neuron Model and Its Application in Oscillatory Associative Memory

2022

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“…Neuron, as an essential element of neural network, can exhibit diverse electrical activities in response to the externally imposed stimuli [1][2][3]. To imitate the biological neuronal dynamics, different mathematical neuron models were presented, including the Hodgkin-Huxley model [4][5][6], two-dimensional (2D) and three-dimensional (3D) Hindmarsh-Rose (HR) models [7,8], memristive HR model with threshold electromagnetic induction [9], fractional-order HR model [10], modified Izhikevich model [11], Morris-Lecar model [12,13], FitzHugh-Nagumo photosensitive model [14], and excitable map-based model [15]. Besides, to develop brain-like hardware devices, different electronic neurons were also developed [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Smooth nonlinear fitting scheme for analog multiplierless implementation of Hindmarsh–Rose neuron model

Cai

Bao

et al. 2021

Nonlinear Dyn

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The Hindmarsh-Rose (HR) neuron model is built to describe the neuron electrical activities. Due to the polynomial nonlinearities, multipliers are required to implement the HR neuron model in analog. In order to avoid the multipliers, this brief presents a novel smooth nonlinear fitting scheme. We first construct two nonlinear fitting functions using the composite hyperbolic tangent functions and then implement an analog multiplierless circuit for the two-dimensional (2D) or three-dimensional (3D) HR neuron model. To exhibit the nonlinear fitting effects, numerical simulations and hardware experiments for the fitted HR neuron model are provided successively. The results show that the fitted HR neuron model with analog multiplierless circuit can display different operation patterns of resting, periodic spiking, and periodic/chaotic bursting, entirely behaving like the original HR neuron model. The analog multiplierless circuit has the advantage of low implementation cost and thereby it might be suitable for the hardware implementation of large-scale neural networks.

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“…The simulation results show that the cycle is similar to the neuron firing cycle and that the duty cycle is 40–70%. Zhang et al (2018) studied the effect of TMAS current density on the action potential of excitatory or inhibitory cortical neurons based on the Izhikevich model. The results showed that with increasing current density, the firing interval of cortical neurons decreases and the firing frequency increases ( Zhang et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

“… Zhang et al (2018) studied the effect of TMAS current density on the action potential of excitatory or inhibitory cortical neurons based on the Izhikevich model. The results showed that with increasing current density, the firing interval of cortical neurons decreases and the firing frequency increases ( Zhang et al, 2018 ). Wang H. et al (2019) used TMAS with different spatial peak time average sound intensities under the condition of a static magnetic field intensity of 0.3 T to stimulate the motor cortex of mice.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Transcranial Magnetoacoustic Stimulation Parameters on the Basal Ganglia-Thalamus Neural Network in Parkinson’s Disease

Zhang

Ling

et al. 2021

Front. Neurosci.

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Objective: Parkinson’s disease (PD) is a degenerative disease of the nervous system that frequently occurs in the aged. Transcranial magnetoacoustic stimulation (TMAS) is a neuronal adjustment method that combines sound fields and magnetic fields. It has the characteristics of high spatial resolution and noninvasive deep brain focusing.Methods: This paper constructed a simulation model of TMAS based on volunteer’s skull computer tomography, phased controlled transducer and permanent magnet. It simulates a transcranial focused sound pressure field with the Westervelt equation and builds a basal ganglia and thalamus neural network model in the PD state based on the Hodgkin-Huxley model.Results: A biased sinusoidal pulsed ultrasonic TMAS induced current with 0.3 T static magnetic field induction and 0.2 W⋅cm–2 sound intensity can effectively modulate PD states with RI ≥ 0.633. The magnitude of magnetic induction strength was changed to 0.2 and 0.4 T. The induced current was the same when the sound intensity was 0.4 and 0.1 W⋅cm–2. And the sound pressure level is in the range of −1 dB (the induced current difference is less than or equal to 0.019 μA⋅cm–2). TMAS with a duty cycle of approximately 50% can effectively modulates the error firings in the PD neural network with a relay reliability not less than 0.633.Conclusion: TMAS can modulates the state of PD.

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Effect of Transcranial Ultrasonic–Magnetic Stimulation on Two Types of Neural Firing Behaviors in Modified Izhikevich Model

Cited by 13 publications

References 14 publications

Memristive Izhikevich Spiking Neuron Model and Its Application in Oscillatory Associative Memory

Memristive Izhikevich Spiking Neuron Model and Its Application in Oscillatory Associative Memory

Smooth nonlinear fitting scheme for analog multiplierless implementation of Hindmarsh–Rose neuron model

The Influence of Transcranial Magnetoacoustic Stimulation Parameters on the Basal Ganglia-Thalamus Neural Network in Parkinson’s Disease

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