On a circuit representation of the Hodgkin and Huxley nerve axon membrane equations

Parodi, Mauro; Storace, Marco

doi:10.1002/(sici)1097-007x(199703/04)25:2<115::aid-cta957>3.0.co;2-#

Cited by 10 publications

(8 citation statements)

References 9 publications

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“…The Hodgkin-Huxley (HH) model (figure 2a, [61]) accounts for the nonlinear, voltagedependent and time-varying nature of the ion channels across the membrane. In this work we use the time-invariant version of the HH [62][63][64] and adopt the ion concentrations for humans [65] to compute the equilibrium voltage generators (E K , E Na , E L ), although any signal generation mechanism amenable to a lumped-element SPICE-like description could be used as well thanks to the mixed-mode device-circuit simulation environment adopted in this work. The HH model should be instantiated for each compartment (a single one is shown in figure 2a).…”

Section: Methodsmentioning

confidence: 99%

Multiscale simulation analysis of passive and active micro/nanoelectrodes for CMOS-based in vitro neural sensing devices

Leva

Palestri

Selmi

2022

Phil. Trans. R. Soc. A.

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Neuron and neural network studies are remarkably fostered by novel stimulation and recording systems, which often make use of biochips fabricated with advanced electronic technologies and, notably, micro- and nanoscale complementary metal-oxide semiconductor (CMOS). Models of the transduction mechanisms involved in the sensor and recording of the neuron activity are useful to optimize the sensing device architecture and its coupling to the readout circuits, as well as to interpret the measured data. Starting with an overview of recently published integrated active and passive micro/nanoelectrode sensing devices for in vitro studies fabricated with modern (CMOS-based) micro-nano technology, this paper presents a mixed-mode device-circuit numerical-analytical multiscale and multiphysics simulation methodology to describe the neuron-sensor coupling, suitable to derive useful design guidelines. A few representative structures and coupling conditions are analysed in more detail in terms of the most relevant electrical figures of merit including signal-to-noise ratio. This article is part of the theme issue ‘Advanced neurotechnologies: translating innovation for health and well-being’.

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Section: Methodsmentioning

confidence: 99%

Multiscale simulation analysis of passive and active micro/nanoelectrodes for CMOS-based in vitro neural sensing devices

Leva

Palestri

Selmi

2022

Phil. Trans. R. Soc. A.

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“…Early models of the heart action potential (AP) have been stemmed from the Hodgkin-Huxley (HH) model [15]. The HH model provides an electric circuit representation for the electrical performance of a nerve axon [16]. In the HH model, membranes are represented as capacitors, and sodium and potassium ions are represented as currents [16].…”

Section: Model Descriptionmentioning

confidence: 99%

“…The HH model provides an electric circuit representation for the electrical performance of a nerve axon [16]. In the HH model, membranes are represented as capacitors, and sodium and potassium ions are represented as currents [16]. D. Noble [15] discusses a cardiac model based on HH that investigates sodium current activity in the Purkinje fiber.…”

Section: Model Descriptionmentioning

confidence: 99%

Model based estimation of QT intervals in non-invasive fetal ECG signals

et al. 2020

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The end timing of T waves in fetal electrocardiogram (fECG) is important for the evaluation of ST and QT intervals which are vital markers to assess cardiac repolarization patterns. Monitoring malignant fetal arrhythmias in utero is fundamental to care in congenital heart anomalies preventing perinatal death. Currently, reliable detection of end of T waves is possible only by using fetal scalp ECG (fsECG) and fetal magnetocardiography (fMCG). fMCG is expensive and less accessible and fsECG is an invasive technique available only during intrapartum period. Another safer and affordable alternative is the non-invasive fECG (nfECG) which can provide similar assessment provided by fsECG and fMECG but with less accuracy (not beat by beat). Detection of T waves using nfECG is challenging because of their low amplitudes and high noise. In this study, a novel model-based method that estimates the end of T waves in nfECG signals is proposed. The repolarization phase has been modeled as the discharging phase of a capacitor. To test the model, fECG signals were collected from 58 pregnant women (age: (34 ± 6) years old) bearing normal and abnormal fetuses with gestational age (GA) 20-41 weeks. QT and QTc intervals have been calculated to test the level of agreement between the model-based and reference values (fsECG and Doppler Ultrasound (DUS) signals) in normal subjects. The results of the test showed high agreement between modelbased and reference values (difference < 5%), which implies that the proposed model could be an alternative method to detect the end of T waves in nfECG signals.

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“…Examples of direct problems include circuit approximations of dynamical systems used to process sequences of images [18], [23] or bio-inspired neural networks based on biological neuron models, such as the Hodgkin-Huxley model of the axon membrane [17], [24], [25]. By resorting to continuation methods [26] applied to smoothed versions of the PWL approximations, we showed in [21] that the PWL technique can provide approximations that are both qualitatively and quantitatively similar to the original systems.…”

Section: Introductionmentioning

confidence: 99%

Piecewise-Linear Identification of Nonlinear Dynamical Systems in View of Their Circuit Implementations

Feo

Storace

2007

IEEE Trans. Circuits Syst. I

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We address here an aspect of the problem concerning circuit implementations of nonlinear dynamical systems that depend on control parameters. In particular, the problem of the identification of such systems is addressed in two steps. The first step uses the state-space reconstruction (through time delay reconstruction associated with principal component analysis) on the basis of scalar time series measured in the systems to be identified. The second step deals with the approximation of the flow in the reconstructed space (through a piecewise-linear approximation technique). The proposed method is first validated with two examples concerning known systems and then applied successfully to two realistic cases.

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On a circuit representation of the Hodgkin and Huxley nerve axon membrane equations

Cited by 10 publications

References 9 publications

Multiscale simulation analysis of passive and active micro/nanoelectrodes for CMOS-based in vitro neural sensing devices

Multiscale simulation analysis of passive and active micro/nanoelectrodes for CMOS-based in vitro neural sensing devices

Model based estimation of QT intervals in non-invasive fetal ECG signals

Piecewise-Linear Identification of Nonlinear Dynamical Systems in View of Their Circuit Implementations

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