White-noise stimulation of the Hodgkin–Huxley model

 We studied the combined influence of noise and constant current stimulations on the Hodgkin–Huxley neuron model through time and frequency analysis of the membrane-potential dynamics. We observed that, in agreement with experimental data (Guttman et al. 1974), at low noise and low constant current stimulation the behavior of the model is well approximated by that of the linearized Hodgkin–Huxley system. Conversely, nonlinearities due to firing dominate at large noise or current stimulations. The transition between the two regimes is abrupt, and takes place in the same range of noise and current intensities as the noise-induced transition characterized by the qualitative change in the stationary distribution of the membrane potential (Tanabe and Pakdaman 2001a). The implications of these results are discussed. Received: 27 July 2001 / Accepted in revised form: 18 December 2001     

Effects of heterogeneity in synaptic conductance between weakly coupled identical neurons

A significant degree of heterogeneity in synaptic conductance is present in neuron to neuron connections. We study the dynamics of weakly coupled pairs of neurons with heterogeneities in synaptic conductance using Wang–Buzsaki and Hodgkin–Huxley model neurons which have Types I and II excitability, respectively. This type of heterogeneity breaks a symmetry in the bifurcation diagrams of equilibrium phase difference versus the synaptic rate constant when compared to the identical case. For weakly coupled neurons coupled with identical values of synaptic conductance a phase locked solution exists for all values of the synaptic rate constant, α. In particular, in-phase and anti-phase solutions are guaranteed to exist for all α. Heterogeneity in synaptic conductance results in regions where no phase locked solution exists and the general loss of the ubiquitous in-phase and anti-phase solutions of the identically coupled case. We explain these results through examination of interaction functions using the weak coupling approximation and an in-depth analysis of the underlying multiple cusp bifurcation structure of the systems of coupled neurons.     

The response of a classical Hodgkin–Huxley neuron to an inhibitory input pulse

A population of uncoupled neurons can often be brought close to synchrony by a single strong inhibitory input pulse affecting all neurons equally. This mechanism is thought to underlie some brain rhythms, in particular gamma frequency (30–80 Hz) oscillations in the hippocampus and neocortex. Here we show that synchronization by an inhibitory input pulse often fails for populations of classical Hodgkin–Huxley neurons. Our reasoning suggests that in general, synchronization by inhibitory input pulses can fail when the transition of the target neurons from rest to spiking involves a Hopf bifurcation, especially when inhibition is shunting, not hyperpolarizing. Surprisingly, synchronization is more likely to fail when the inhibitory pulse is stronger or longer-lasting. These findings have potential implications for the question which neurons participate in brain rhythms, in particular in gamma oscillations.     

Modelled temperature-dependent excitability behaviour of a single ranvier node for a human peripheral sensory nerve fibre

The objective of this study was to determine whether the Hodgkin–Huxley model for unmyelinated nerve fibres could be modified to predict excitability behaviour at Ranvier nodes. Only the model parameters were modified to those of human, with the equations left unaltered. A model of a single Ranvier node has been developed as part of a larger model to describe excitation behaviour in a generalised human peripheral sensory nerve fibre. Parameter values describing the ionic and leakage conductances, corresponding equilibrium potentials, resting membrane potential and membrane capacitance of the original Hodgkin–Huxley model were modified to reflect the corresponding parameter values for human. Parameter temperature dependence was included. The fast activating potassium current kinetics were slowed down to represent those of a slow activating and deactivating potassium current, which do not inactivate. All calculations were performed in MATLAB^TM. Action potential shape and amplitude were satisfactorily predicted at 20, 25 and 37°C, and were not influenced by activation or deactivation of the slow potassium current. The calculated chronaxie time constant was 65.5 μs at 37°C. However, chronaxie times were overestimated at temperatures lower than body temperature.     

Simple models for excitable and oscillatory neural networks

 Chains of coupled oscillators of simple “rotator” type have been used to model the central pattern generator (CPG) for locomotion in lamprey, among numerous applications in biology and elsewhere. In this paper, motivated by experiments on lamprey CPG with brainstem attached, we investigate a simple oscillator model with internal structure which captures both excitable and bursting dynamics. This model, and that for the coupling functions, is inspired by the Hodgkin–Huxley equations and two-variable simplifications thereof. We analyse pairs of coupled oscillators with both excitatory and inhibitory coupling. We also study traveling wave patterns arising from chains of oscillators, including simulations of “body shapes” generated by a double chain of oscillators providing input to a kinematic musculature model of lamprey.. Received: 25 November 1996 / Revised version: 9 December 1997     

Invariant manifold reductions for Markovian ion channel dynamics

We show that many Markov models of ion channel kinetics have globally attracting stable invariant manifolds, even when the Markov process is time dependent. The primary implication of this is that, since the dimension of the invariant manifold is often substantially smaller than the full master equation system, simulations of ion channel kinetics can be substantially simplified, with no approximation. We show that this applies to certain models of potassium channels, sodium channels, ryanodine receptors and IP₃ receptors. We also use this to show that the original Hodgkin–Huxley formulations of potassium channel conductance and sodium channel conductance are the exact solutions of full Markov models for these channels.      

Simulation of networks of spiking neurons: A review of tools and strategies

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin–Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks. Action Editor: Barry J. Richmond     

Dynamic modeling for flow-activated chloride-selective membrane current in vascular endothelial cells

In this paper, a dynamic model is proposed to quantify the relationship between fluid flow and Cl⁻-selective membrane current in vascular endothelial cells (VECs). It is assumed that the external shear stress would first induce channel deformation in VECs. This deformation could activate the Cl⁻ channels on the membrane, thus allowing Cl⁻ transport across the membrane. A modified Hodgkin–Huxley model is embedded into our dynamic system to describe the electrophysiological properties of the membrane, such as the Cl⁻-selective membrane current (I), voltage (V) and conductance. Three flow patterns, i. e., steady flow, oscillatory flow, and pulsatile flow, are applied in our simulation studies. When the extracellular Cl⁻ concentration is constant, the I-V characteristics predicted by our dynamic model shows strong consistency with the experimental observations. It is also interesting to note that the Cl⁻ currents under different flow patterns show some differences, indicating that VECs distinguish among and respond differently to different types of flows. When the extracellular Cl⁻ concentration keeps constant or varies slowly with time (i.e. oscillates at 0.02 Hz), the convection and diffusion of Cl⁻ in extracellular space can be ignored and the Cl⁻ current is well captured by the modified Hodgkin–Huxley model alone. However, when the extracellular Cl⁻ varies fast (i.e., oscillates at 0.2 Hz), the convection and diffusion effect should be considered because the Cl⁻ current dynamics is different from the case where the convection-diffusion effect is simply ignored. The proposed dynamic model along with the simulation results could not only provide more insights into the flow-regulated electrophysiological behavior of the cell membrane but also help to reveal new findings in the electrophysiological experimental investigations of VECs in response to dynamic flow and biochemical stimuli.     

Dynamical estimation of neuron and network properties II: path integral Monte Carlo methods

Hodgkin–Huxley (HH) models of neuronal membrane dynamics consist of a set of nonlinear differential equations that describe the time-varying conductance of various ion channels. Using observations of voltage alone we show how to estimate the unknown parameters and unobserved state variables of an HH model in the expected circumstance that the measurements are noisy, the model has errors, and the state of the neuron is not known when observations commence. The joint probability distribution of the observed membrane voltage and the unobserved state variables and parameters of these models is a path integral through the model state space. The solution to this integral allows estimation of the parameters and thus a characterization of many biological properties of interest, including channel complement and density, that give rise to a neuron’s electrophysiological behavior. This paper describes a method for directly evaluating the path integral using a Monte Carlo numerical approach. This provides estimates not only of the expected values of model parameters but also of their posterior uncertainty. Using test data simulated from neuronal models comprising several common channels, we show that short (<50 ms) intracellular recordings from neurons stimulated with a complex time-varying current yield accurate and precise estimates of the model parameters as well as accurate predictions of the future behavior of the neuron. We also show that this method is robust to errors in model specification, supporting model development for biological preparations in which the channel expression and other biophysical properties of the neurons are not fully known.     

Complex nonlinear dynamics of the Hodgkin-Huxley equations induced by time scale changes

 The Hodgkin–Huxley equations with a slight modification are investigated, in which the inactivation process (h) of sodium channels or the activation process of potassium channels (n) is slowed down. We show that the equations produce a variety of action potential waveforms ranging from a plateau potential, such as in heart muscle cells, to chaotic bursting firings. When h is slowed down – differently from the case of n variable being slow – chaotic bursting oscillations are observed for a wide range of parameter values although both variables cause a decrease in the membrane potential. The underlying nonlinear dynamics of various action potentials are analyzed using bifurcation theory and a so-called slow–fast decomposition analysis. It is shown that a simple topological property of the equilibrium curves of slow and fast subsystems is essential to the production of chaotic oscillations, and this is the cause of the large difference in global firing characteristics between the h-slow and n-slow cases. Received: 9 August 2000 / Accepted in revised form: 10 January 2001     

Gamma oscillations as a mechanism for selective information transmission

In the past decades, many studies have focussed on the relation between the input and output of neurons with the aim to understand information processing by neurons. A particular aspect of neuronal information, which has not received much attention so far, concerns the problem of information transfer when a neuron or a population of neurons receives input from two or more (populations of) neurons, in particular when these (populations of) neurons carry different types of information. The aim of the present study is to investigate the responses of neurons to multiple inputs modulated in the gamma frequency range. By a combination of theoretical approaches and computer simulations, we test the hypothesis that enhanced modulation of synchronized excitatory neuronal activity in the gamma frequency range provides an advantage over a less synchronized input for various types of neurons. The results of this study show that the spike output of various types of neurons [i.e. the leaky integrate and fire neuron, the quadratic integrate and fire neuron and the Hodgkin–Huxley (HH) neuron] and that of excitatory–inhibitory coupled pairs of neurons, like the Pyramidal Interneuronal Network Gamma (PING) model, is highly phase-locked to the larger of two gamma-modulated input signals. This implies that the neuron selectively responds to the input with the larger gamma modulation if the amplitude of the gamma modulation exceeds that of the other signals by a certain amount. In that case, the output of the neuron is entrained by one of multiple inputs and that other inputs are not represented in the output. This mechanism for selective information transmission is enhanced for short membrane time constants of the neuron.     

Modeling the pre- and post-synaptic components involved in the synaptic modification between cones and horizontal cells in carp retina

In retinal synapses between cones and luminosity type horizontal cells (LHC), it was previously found in this laboratory that repetitive red flashes progressively strengthened the LHC’s response to red flash, whereas weakened the LHC’s response to green flash; repetitive green flash remarkably depressed the LHC’s red response, but caused little changes in the cell’s green response. However, the detailed mechanisms underlying these phenomena are not entirely clear. In the present study, based on an ion-channel model described mainly in the form of Hodgkin–Huxley equations, possible mechanisms of the short-term synaptic modification are investigated. The simulation results suggest that: (1) the auto-enhancement effect might be induced by the Ca²⁺-dependent process on the post-synaptic AMPA receptors, which could lead to changes of the ionic channel’s properties; (2) the asymmetric response to red- and green-flashes and the mutual-chromatic suppression effects might be attributed to the regulatory effects on the presynaptic glutamate release.     

Random perturbations of spiking activity in a pair of coupled neurons

We examine the effects of stochastic input currents on the firing behaviour of two coupled Type 1 or Type 2 neurons. In Hodgkin–Huxley model neurons with standard parameters, which are Type 2, in the bistable regime, synaptic transmission can initiate oscillatory joint spiking, but white noise can terminate it. In Type 1 cells (models), typified by a quadratic integrate and fire model, synaptic coupling can cause oscillatory behaviour in excitatory cells, but Gaussian white noise can again terminate it. We locally determine an approximate basin of attraction, of the periodic orbit and explain the firing behaviour in terms of the effects of noise on the probability of escape of trajectories from      

Modelled temperature-dependent excitability behaviour of a generalised human peripheral sensory nerve fibre

The objective of this study was to determine if a recently developed human Ranvier node model, which is based on a modified version of the Hodgkin–Huxley model, could predict the excitability behaviour in human peripheral sensory nerve fibres with diameters ranging from 5.0 to 15.0 μm. The Ranvier node model was extended to include a persistent sodium current and was incorporated into a generalised single cable nerve fibre model. Parameter temperature dependence was included. All calculations were performed in Matlab. Sensory nerve fibre excitability behaviour characteristics predicted by the new nerve fibre model at different temperatures and fibre diameters compared well with measured data. Absolute refractory periods deviated from measured data, while relative refractory periods were similar to measured data. Conduction velocities showed both fibre diameter and temperature dependence and were underestimated in fibres thinner than 12.5 μm. Calculated strength–duration time constants ranged from 128.5 to 183.0 μs at 37°C over the studied nerve fibre diameter range, with chronaxie times about 30% shorter than strength–duration time constants. Chronaxie times exhibited temperature dependence, with values overestimated by a factor 5 at temperatures lower than body temperature. Possible explanations include the deviated absolute refractory period trend and inclusion of a nodal strangulation relationship. At the time of this research J. E. Smit was with the University of Pretoria.     

Comparison of different neuron models to conductance-based post-stimulus time histograms obtained in cortical pyramidal cells using dynamic-clamp in vitro

A wide diversity of models have been proposed to account for the spiking response of central neurons, from the integrate-and-fire (IF) model and its quadratic and exponential variants, to multiple-variable models such as the Izhikevich (IZ) model and the well-known Hodgkin–Huxley (HH) type models. Such models can capture different aspects of the spiking response of neurons, but there is few objective comparison of their performance. In this article, we provide such a comparison in the context of well-defined stimulation protocols, including, for each cell, DC stimulation, and a series of excitatory conductance injections, arising in the presence of synaptic background activity. We use the dynamic-clamp technique to characterize the response of regular-spiking neurons from guinea-pig visual cortex by computing families of post-stimulus time histograms (PSTH), for different stimulus intensities, and for two different background activities (low- and high-conductance states). The data obtained are then used to fit different classes of models such as the IF, IZ, or HH types, which are constrained by the whole data set. This analysis shows that HH models are generally more accurate to fit the series of experimental PSTH, but their performance is almost equaled by much simpler models, such as the exponential or pulse-based IF models. Similar conclusions were also reached by performing partial fitting of the data, and examining the ability of different models to predict responses that were not used for the fitting. Although such results must be qualified by using more sophisticated stimulation protocols, they suggest that nonlinear IF models can capture surprisingly well the response of cortical regular-spiking neurons and appear as useful candidates for network simulations with conductance-based synaptic interactions.     

Visual perception of ambiguous figures: synchronization based neural models

We develop and study two neural network models of perceptual alternations. Both models have a star-like architecture of connections with a central element connected to a set of peripheral elements. A particular perception is simulated in terms of partial synchronization between the central element and some sub-group of peripheral elements. The first model is constructed from phase oscillators and the mechanism of perceptual alternations is based on chaotic intermittency under fixed parameter values. Similar to experimental evidence, the distribution of times between perceptual alternations is represented by the gamma distribution. The second model is built of spiking neurons of the Hodgkin–Huxley type. The mechanism of perceptual alternations is based on plasticity of inhibitory synapses which increases the inhibition from the central unit to the neural assembly representing the current percept. As a result another perception is formed. Simulations show that the second model is in good agreement with behavioural data on switching times between percepts of ambiguous figures and with experimental results on binocular rivalry of two and four percepts. This article is part of a special issue on Neuronal Dynamics of Sensory Coding. This special issue is in honour of Professor Pepe Segundo who is one of the pioneers in the study of neural coding. Pepe has been an active participant in many Neural Coding Workshops sharing his great knowledge and experience of research in this field. I (R. Borisyuk) was very happy to meet Pepe for the first time in Prague when attending the first Neural Coding Workshop in 1995. From that time we regularly met at Neural Coding Workshops and these meetings have always been very stimulating and fruitful for my research. Remarkably, the first paper I studied at the beginning of my scientific career was a seminal paper by Moore et al. (1970). For me, this paper provided a great opportunity to learn the basic statistical techniques for the analysis of multiple spike trains and neural coding. According to the Institute of Scientific Information, this paper has been cited 380 times! This exciting paper has inspired my research into the synaptic and functional connectivity of neural circuits derived from spike-train recordings (Borisyuk et al. 1985; Stuart et al. 2005) and guided my search for new ideas on neural coding.     

On the effects of spatial heterogeneity on the persistence of interacting species

 The dynamics of two interacting theoretical populations inhabiting a heterogeneous environment are modelled by a system of two weakly coupled reaction–diffusion equations having spatially dependent reaction terms. Longterm persistence of both populations is guaranteed by an invasibility condition, which is itself expressed via the signs of certain eigenvalues of related linear elliptic operators with spatially dependent lowest order coefficients. The effects of change in these coefficients upon the eigenvalues are here exploited to study the effects of spatial heterogeneity on the persistence of interacting species through two particular ecological topics of interest. The first concerns when the location of favorable hunting grounds within the overall environment does or does not affect the success of a predator in predator–prey models, while the second concerns cases of competition models in which the outcome of competition in a spatially varying environment differs from that which would be expected in a spatially homogeneous environment. Received: 9 June 1997