Frontiers in Neuroscience · 2021 · 34 citations · 27 references
Natural SciencesBiophysicsElectrical EngineeringComputational NeuroscienceNeural CircuitryNeurobiophysicsAction Potential DynamicsBiophysical Neuron ModelingMemristive MaterialsNeural Circuit ModelingNeuronal DynamicsSynaptic TransmissionAction Potential GenerationAction Potential ModelingCurrent DensityHodgkin-huxley ModelsIonic ConductionNeuron DynamicsSpiking ActivitySpiking Neuron Models
The Hodgkin-Huxley (HH) spiking neuron model reproduces the dynamic characteristics of the neuron by mimicking the action potential, ionic channels, and spiking behaviors. The memristor is a nonlinear device with variable resistance. In this paper, the memristor is introduced to the HH spiking model, and the memristive Hodgkin-Huxley spiking neuron model (MHH) is presented. We experimentally compare the HH spiking model and the MHH spiking model by applying different stimuli. First, the individual current pulse is injected into the HH and MHH spiking models. The comparison between action potentials, current densities, and conductances is carried out. Second, the reverse single pulse stimulus and a series of pulse stimuli are applied to the two models. The effects of current density and action time on the production of the action potential are analyzed. Finally, the sinusoidal current stimulus acts on the two models. The various spiking behaviors are realized by adjusting the frequency of the sinusoidal stimulus. We experimentally demonstrate that the MHH spiking model generates more action potential than the HH spiking model and takes a short time to change the memductance. The reverse stimulus cannot activate the action potential in both models. The MHH spiking model performs smoother waveforms and a faster speed to return to the resting potential. The larger the external stimulus, the faster action potential generated, and the more noticeable change in conductances. Meanwhile, the MHH spiking model shows the various spiking patterns of neurons.
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Dmitri B. Strukov, Gregory S. Snider, Duncan R. Stewart et al. · Nature · 2008 · 11.5K citations
Engineering, Electrical Engineering, Electronic Materials +5
Memristor-The missing circuit element
Leon O. Chua · IEEE Transactions on Circuit Theory · 1971 · 10K citations
Memristive devices for computing
J. Joshua Yang, Dmitri B. Strukov, Duncan R. Stewart · Nature Nanotechnology · 2012 · 3.8K citations
Engineering, Computer Engineering, Electrical Engineering +11