Biological neurons can be excited to maintain certain firing patterns following different external stimuli,and similar changes in electrical activities can be reproduced in some neural circuits by applying an external...Biological neurons can be excited to maintain certain firing patterns following different external stimuli,and similar changes in electrical activities can be reproduced in some neural circuits by applying an external voltage.Generic neural circuits are composed of capacitors,induction coils,resistors,and nonlinear resistors,and continuous energy exchange between the capacitive and inductive components is crucial for preserving output voltages.Incorporating nonlinear elements causes interactions between the charge flow across the capacitor and the induced electromotive force on the inductor.It is a challenge to explore the occurrence of nonlinear oscillation and coherence resonance in a neural circuit without using a capacitor and nonlinear resistor,and it considers the case lack of electric field energy.In this paper,a simple neural circuit is proposed that combines two inductors,one magnetic flux-controlled memristor(MFCM),and three resistors,with two constant voltage sources in the branch circuits used as reverse potentials in the ion channels.The field energy has an exact form,and it is stored in the circuit components as a magnetic field.Scale transformation is applied on the circuit equations and field energy function to obtain equivalent dimensionless forms of the memristive neuron and Hamilton energy.The reference values for the physical time and capacitance are represented by an appropriate combination of resistance and inductance,because the capacitance value is unavailable.The memristive neuron without capacitive effect still shows similar firing patterns,and coherence resonance is induced under noisy excitation.The emergence of coherence resonance can be predicted by calculating the distribution of the average energy<H>versus noise intensity,and the value for<H>reaches a maximum under coherence resonance.Finally,an adaptive law for parameter growth under energy control is proposed to control mode transitions in the electrical activity.The methodology and results of this work offer insights into the oscillatory mechanism of neural circuits,and showcase how magnetic field control can be used to manage neural activations.展开更多
The ion channel in neurons is the basic component of signal transmission in the nervous system.The ion channel has important effects on the potential of neuron release and dynamic behavior in neural networks.Ion chann...The ion channel in neurons is the basic component of signal transmission in the nervous system.The ion channel has important effects on the potential of neuron release and dynamic behavior in neural networks.Ion channels control the flow of ions into and out of the cell membrane to form an ion current,which makes the excitable membrane produce special potential changes and become the basis of nerve and muscle activity.The blockage of ion channels has a significant effect on the dynamics of neurons and networks.Therefore,it is very meaningful to study the influence of ion channels on neuronal dynamics.In this work,a hybrid ion channel is designed by connecting a charge-controlled memristor(CCM)with an inductor in series,and a magnetic flux-controlled memristor(MFCM),capacitor,and nonlinear resistor are connected in parallel with the mixed ion channel to obtain the memristor neural circuit.Furthermore,the oscillator model with a hybrid ion channel and its energy function are calculated,and a map neuron is obtained by linearizing the neuron oscillator model.In addition,an adaptive regulation method is designed to explore the adaptive regulation of energy on the dynamic behaviors of the map neuron.The results show that the dynamics of a map neuron with a hybrid ion channel can be controlled by parameters and external magnetic fields.This study is also used to research synchronization between map neurons and collective behaviors in the map neurons network.展开更多
Synaptic plasticity can greatly affect the firing behavior of neural networks,and it specifically refers to changes in the strength,morphology,and function of synaptic connections.In this paper,a novel memristor model...Synaptic plasticity can greatly affect the firing behavior of neural networks,and it specifically refers to changes in the strength,morphology,and function of synaptic connections.In this paper,a novel memristor model,which can be configured as a volatile and nonvolatile memristor by adjusting its internal parameter,is proposed to mimic the short-term and long-term synaptic plasticity.Then,a bi-neuron network model,with the proposed memristor serving as a coupling synapse and the external electromagnetic radiation being emulated by the flux-controlled memristors,is established to elucidate the effects of short-term and long-term synaptic plasticity on firing activity of the neuron network.The resultant seven-dimensional(7D)neuron network has no equilibrium point and its hidden dynamical behavior is revealed by phase diagram,time series,bifurcation diagram,Lyapunov exponent spectrum,and two-dimensional(2D)dynamic map.Our results show the short-term and long-term plasticity can induce different bifurcation scenarios when the coupling strength increases.In addition,memristor synaptic plasticity has a great influence on the distribution of firing patterns in the parameter space.More interestingly,when exploring the synchronous firing behavior of two neurons,the two neurons can gradually achieve phase synchronization as the coupling strength increases along the opposite directions under two different memory attributes.Finally,a microcontroller-based hardware system is implemented to verify the numerical simulation results.展开更多
The FitzHugh–Nagumo neuron circuit integrates a piezoelectric ceramic to form a piezoelectric sensing neuron,which can capture external sound signals and simulate the auditory neuron system.Two piezoelectric sensing ...The FitzHugh–Nagumo neuron circuit integrates a piezoelectric ceramic to form a piezoelectric sensing neuron,which can capture external sound signals and simulate the auditory neuron system.Two piezoelectric sensing neurons are coupled by a parallel circuit consisting of a Josephson junction and a linear resistor,and a binaural auditory system is established.Considering the non-singleness of external sound sources,the high–low frequency signal is used as the input signal to study the firing mode transition and synchronization of this system.It is found that the angular frequency of the high–low frequency signal is a key factor in determining whether the dynamic behaviors of two coupled neurons are synchronous.When they are in synchronization at a specific angular frequency,the changes in physical parameters of the input signal and the coupling strength between them will not destroy their synchronization.In addition,the firing mode of two coupled auditory neurons in synchronization is affected by the characteristic parameters of the high–low frequency signal rather than the coupling strength.The asynchronous dynamic behavior and variations in firing modes will harm the auditory system.These findings could help determine the causes of hearing loss and devise functional assistive devices for patients.展开更多
基金supported by the National Natural Science Foundation of China(No.62361037).
文摘Biological neurons can be excited to maintain certain firing patterns following different external stimuli,and similar changes in electrical activities can be reproduced in some neural circuits by applying an external voltage.Generic neural circuits are composed of capacitors,induction coils,resistors,and nonlinear resistors,and continuous energy exchange between the capacitive and inductive components is crucial for preserving output voltages.Incorporating nonlinear elements causes interactions between the charge flow across the capacitor and the induced electromotive force on the inductor.It is a challenge to explore the occurrence of nonlinear oscillation and coherence resonance in a neural circuit without using a capacitor and nonlinear resistor,and it considers the case lack of electric field energy.In this paper,a simple neural circuit is proposed that combines two inductors,one magnetic flux-controlled memristor(MFCM),and three resistors,with two constant voltage sources in the branch circuits used as reverse potentials in the ion channels.The field energy has an exact form,and it is stored in the circuit components as a magnetic field.Scale transformation is applied on the circuit equations and field energy function to obtain equivalent dimensionless forms of the memristive neuron and Hamilton energy.The reference values for the physical time and capacitance are represented by an appropriate combination of resistance and inductance,because the capacitance value is unavailable.The memristive neuron without capacitive effect still shows similar firing patterns,and coherence resonance is induced under noisy excitation.The emergence of coherence resonance can be predicted by calculating the distribution of the average energy<H>versus noise intensity,and the value for<H>reaches a maximum under coherence resonance.Finally,an adaptive law for parameter growth under energy control is proposed to control mode transitions in the electrical activity.The methodology and results of this work offer insights into the oscillatory mechanism of neural circuits,and showcase how magnetic field control can be used to manage neural activations.
基金supported by the National Science Basic Research Program of Shaanxi(Grant No.2023-JC-QN-0087)。
文摘The ion channel in neurons is the basic component of signal transmission in the nervous system.The ion channel has important effects on the potential of neuron release and dynamic behavior in neural networks.Ion channels control the flow of ions into and out of the cell membrane to form an ion current,which makes the excitable membrane produce special potential changes and become the basis of nerve and muscle activity.The blockage of ion channels has a significant effect on the dynamics of neurons and networks.Therefore,it is very meaningful to study the influence of ion channels on neuronal dynamics.In this work,a hybrid ion channel is designed by connecting a charge-controlled memristor(CCM)with an inductor in series,and a magnetic flux-controlled memristor(MFCM),capacitor,and nonlinear resistor are connected in parallel with the mixed ion channel to obtain the memristor neural circuit.Furthermore,the oscillator model with a hybrid ion channel and its energy function are calculated,and a map neuron is obtained by linearizing the neuron oscillator model.In addition,an adaptive regulation method is designed to explore the adaptive regulation of energy on the dynamic behaviors of the map neuron.The results show that the dynamics of a map neuron with a hybrid ion channel can be controlled by parameters and external magnetic fields.This study is also used to research synchronization between map neurons and collective behaviors in the map neurons network.
基金Project supported by the National Natural Science Foundations of China(Grant Nos.62171401 and 62071411)。
文摘Synaptic plasticity can greatly affect the firing behavior of neural networks,and it specifically refers to changes in the strength,morphology,and function of synaptic connections.In this paper,a novel memristor model,which can be configured as a volatile and nonvolatile memristor by adjusting its internal parameter,is proposed to mimic the short-term and long-term synaptic plasticity.Then,a bi-neuron network model,with the proposed memristor serving as a coupling synapse and the external electromagnetic radiation being emulated by the flux-controlled memristors,is established to elucidate the effects of short-term and long-term synaptic plasticity on firing activity of the neuron network.The resultant seven-dimensional(7D)neuron network has no equilibrium point and its hidden dynamical behavior is revealed by phase diagram,time series,bifurcation diagram,Lyapunov exponent spectrum,and two-dimensional(2D)dynamic map.Our results show the short-term and long-term plasticity can induce different bifurcation scenarios when the coupling strength increases.In addition,memristor synaptic plasticity has a great influence on the distribution of firing patterns in the parameter space.More interestingly,when exploring the synchronous firing behavior of two neurons,the two neurons can gradually achieve phase synchronization as the coupling strength increases along the opposite directions under two different memory attributes.Finally,a microcontroller-based hardware system is implemented to verify the numerical simulation results.
基金Project supported by the National Natural Science Foundation of China(Grant No.11605014)。
文摘The FitzHugh–Nagumo neuron circuit integrates a piezoelectric ceramic to form a piezoelectric sensing neuron,which can capture external sound signals and simulate the auditory neuron system.Two piezoelectric sensing neurons are coupled by a parallel circuit consisting of a Josephson junction and a linear resistor,and a binaural auditory system is established.Considering the non-singleness of external sound sources,the high–low frequency signal is used as the input signal to study the firing mode transition and synchronization of this system.It is found that the angular frequency of the high–low frequency signal is a key factor in determining whether the dynamic behaviors of two coupled neurons are synchronous.When they are in synchronization at a specific angular frequency,the changes in physical parameters of the input signal and the coupling strength between them will not destroy their synchronization.In addition,the firing mode of two coupled auditory neurons in synchronization is affected by the characteristic parameters of the high–low frequency signal rather than the coupling strength.The asynchronous dynamic behavior and variations in firing modes will harm the auditory system.These findings could help determine the causes of hearing loss and devise functional assistive devices for patients.
