A modified cable model analysis of microscopic axonal and dendritic processes

The computational background for analysing the passive, subthreshold properties of fine-scale ramifications such as "en-passant" and "terminal ladder" type chains of boutons and spiny dendrites is presented. The segment-by-segment approximation of a cable composed of serial or parallel chains of identical units (modules) is based on the cable representations of boutons, axon, spines and dendrit. Pulse response in the time domain is evaluated from the narrow-bandwidth, recursive estimation of the input and transfer impedances by means of inverse Laplace transformation. The shape of the voltage transients in semi-infinite chain of cable units is found by the input impedance computed under the equilibrium condition. The model predicts differences of subthreshold responses in relation to a change in modular geometry or membrane electrical parameters. The results may help in finding the relationship between the physical and electrotonical geometries of nerve cells with non-smooth processes. The smoothing procedure gives a possibility for the functional unification and simplification of those fine-scale processes of nerve cells where the characteristic space constants are much greater than the intersynaptic distances.     

The interspike interval of a cable model neuron with white noise input

The firing time of a cable model neuron in response to white noise current injection is investigated with various methods. The Fourier decomposition of the depolarization leads to partial differential equations for the moments of the firing time. These are solved by perturbation and numerical methods, and the results obtained are in excellent agreement with those obtained by Monte Carlo simulation. The convergence of the random Fourier series is found to be very slow for small times so that when the firing time is small it is more efficient to simulate the solution of the stochastic cable equation directly using the two different representations of the Green's function, one which converges rapidly for small times and the other which converges rapidly for large times. The shape of the interspike interval density is found to depend strongly on input position. The various shapes obtained for different input positions resemble those for real neurons. The coefficient of variation of the interspike interval decreases monotonically as the distance between the input and trigger zone increases. A diffusion approximation for a nerve cell receiving Poisson input is considered and input/output frequency relations obtained for different input sites. The cases of multiple trigger zones and multiple input sites are briefly discussed.     

A mathematical model with a modified logistic approach for singly peaked population processes

When a small number of individuals of a single species are confined in a closed space with a limited amount of indispensable resources, breeding may start initially under suitable conditions, and after peaking, the population should go extinct as the resources are exhausted. Starting with the logistic equation and assuming that the carrying capacity of the environment is a function of the amount of resources, a mathematical model describing such a pattern of population change is obtained. An application of this model to a typical set of population records, that of deer herds by V. B. Scheffer (1951, Sci. Monthly 73, 356-362) and E. C. O'Roke and F. N. Hamerstrom (1948, J. Wildlife Management 12, 78-86), yields estimates of the initial amount of indispensable food and its availability or nutritional efficiency which were previously unspecified.     

A cable model for coupled neurons with somatic gap junctions

A cable model is presented for a pair of electrotonically coupled neurons to investigate the spatial effects of soma-somatic gap junctions. The model extends that of Poznanski et al.(1995) in which each neuron is represented by a tapered equivalent cable attached to an isopotential soma with the two somas being electrically coupled. The model is posed generally, so that both active and passive properties can be considered. In the active case a system of nonlinear integral equations is derived for the voltage, whilst in the passive case these have an exact solution that also holds for inputs modelled as synaptic reversal potentials. Analytical and numerical methods are used to examine the sensitivity of the soma potentials (in particular) to the coupling resistance.     

A neuron model with fluid properties for solving labyrinthian puzzle

Hopfield presented a neural network with continuous states, associated with an analogue circuit (1984). On the condition that the weights keep and for ij, the potential u _iof the neurons in the system can be of the properties like those of fluid and the communication among neurons is the result of the flowing of the potential like the flowing of fluid (ideal liquid) affected only by gravity. This means that the flowing of the potential among neurons is from higher level to lower level and that the cause for the varing of a neuron in its state is due to that the net input flowrate of all the potential to the neuron is nonzero. Therefore, the system has a quite clear convergent process and, has a unique stable state. Analysing the possible stable state of the system is based on both the flowrate rule and the invariability in volume satisfied by the potential. The experiment of solving labyrinthian puzzle shows that the neural system with fluid properties is well-done to intelligence search problem.     

The somatic shunt cable model for neurons.

The derivation of the equations for an electrical model of nerve cells is presented. The model consists of an equivalent cylinder, a lumped somatic impedance, and a variable shunt at the soma. This shunt was introduced to take into account the fast voltage decays observed following the injections of current pulses in some motoneurons and hippocampal granule cells that could not be explained by existing models. The shunt can be interpreted either by penetration damage with the electrode or by a lower membrane specific resistance at the soma than in the dendrites. A solution of the model equations is presented that allows the estimation of the electrotonic length L, the membrane time constant tau m, the dendritic dominance ratio rho, and the shunt parameter epsilon, based only on the measurement of the first two coefficients and time constants in the multiexponential voltage response to injected current pulses.     

A modified cable formalism for modeling neuronal membranes at high frequencies

Intracellular recordings of cortical neurons in vivo display intense subthreshold membrane potential (V_m) activity. The power spectral density of the V_m displays a power-law structure at high frequencies (>50 Hz) with a slope of ∼−2.5. This type of frequency scaling cannot be accounted for by traditional models, as either single-compartment models or models based on reconstructed cell morphologies display a frequency scaling with a slope close to −4. This slope is due to the fact that the membrane resistance is short-circuited by the capacitance for high frequencies, a situation which may not be realistic. Here, we integrate nonideal capacitors in cable equations to reflect the fact that the capacitance cannot be charged instantaneously. We show that the resulting nonideal cable model can be solved analytically using Fourier transforms. Numerical simulations using a ball-and-stick model yield membrane potential activity with similar frequency scaling as in the experiments. We also discuss the consequences of using nonideal capacitors on other cellular properties such as the transmission of high frequencies, which is boosted in nonideal cables, or voltage attenuation in dendrites. These results suggest that cable equations based on nonideal capacitors should be used to capture the behavior of neuronal membranes at high frequencies.     

System identification based on point processes and correlation densities. I. The nonrefractory neuron model

This paper discusses a neuron model that transforms an input point process into an output point process. The model is composed of two stages: a linear filter operates on the input process, whereafter an instantaneous nonlinearity generates the output point process. If the pulse generator is exponential, it is possible to derive formulas that explicitly express the output autocorrelation and input-output cross-correlation densities into the connectivity function and input autocorrelation densities. Alternatively these formulas may be used to derive connectivity from correlation densities. This identification method is tested on computer simulations of the model.     

A generalized tapering equivalent cable model for dendritic neurons

A mathematical model has been developed which collapses a dendritic neuron of complex geometry into a single electrotonically tapering equivalent cable. The modified cable equation governing the transient distribution of subthreshold membrane potential in a branching tree is transformed, becoming amenable to analytic solution. This transformation results in a Riccati differential equation whose six solutions (expressed in terms of elementary functions) control the amount and degree of taper found in the equivalent cable model. To illustrate the theory, an analytic solution (in series form) of the modified cable equation is obtained for a voltage-clamp present at the soma of a quadratically tapering equivalent cable whose distal end is sealed.     

Structured coalescent processes from a modified Moran model with large offspring numbers

Structured coalescent processes are derived for the finite island model under a migration mechanism that conserves the subpopulation sizes. The underlying population model is a modified Moran model in which the reproducing individual can have very many offspring with some probability. Convergence to a structured coalescent process results when assuming that migration follows a coalescent timescale which can be much shorter than the usual Wright–Fisher timescale. Three different limit processes are possible depending on the coalescent timescale, two of which allow multiple mergers of ancestral lines. The expected time to most recent common ancestor, and the expected total size of the genealogy, of balanced and unbalanced samples can be very similar, even when migration is low, if the coalescent process allows multiple mergers. The expected total size increases almost linearly with sample size in some cases. The results have implications for inference about genetic population structure.     

A probabilistic model for the establishment of neuron polarity

The main aim of this paper is to present a simple probabilistic model for the early stage of neuron growth: the specification on an axon out of several initially similar neurites. The model is a Markov process with competition between the growing neurites, wherein longer objects have more chances to grow, and parameter alpha determines the intensity of the competition. For alpha > 1 the model provides results which are qualitatively similar to the experimental ones, i.e. selection of one rapidly elongating axon out of several neurites while other less successful neurites stop growing at some random time. Rigorous mathematical proofs are given.     

A spiking neuron model for synchronous flashing of fireflies

Certain species of fireflies show a group behavior of synchronous flashing. Their synchronized and rhythmic flashing has received much attention among many researchers, and there has been a study of biological models for their entrainment of flashing. The synchronous behavior of fireflies resembles the firing synchrony of integrate-and-fire neurons with excitatory or inhibitory connections. This paper shows an analysis of spiking neurons specialized for a firefly flashing model, and provides simulation results of multiple neurons with various transmission delays and coupling strengths. It also explains flashing patterns of some firefly species and examines the synchrony conditions depending on transmission delays and coupling strengths.     

A model of a thalamic neuron

We modify our recent three equilibrium-point model of neuronal bursting by a means of a small deformation of the nullclines in the x-y phase plane to give a model that can have as many as five equilibrium points. In this model the middle stable equilibrium point (e.p.) is separated from the outer stable and unstable e.ps by two saddle points. If the system is started at rest at the middle stable e.p. it has the following complex properties: A short suprathreshold current pulse switches the model from a silent state to a bursting state, or to give a single burst, depending on the choice of parameters. A subthreshold depolarizing current step gives a passive response at rest, but if the model is either constantly hyperpolarized or constantly depolarized, then the same current step gives different active responses. At a hyperpolarized level this consists of a burst response that shows refractoriness. At a depolarized level it consists of tonic firing with a linear frequency--current relationship. Hyperpolarization from rest is followed by post-inhibitory rebound. The model responds in a unique and characteristic way to an applied current ramp. These properties are very similar to those that have been recently recorded intracellularly from neurons in the mammalian thalamus. In the x-y phase plane our models of the repetitively firing neuron, the bursting neuron and the thalamic neuron form a progression of models in which the y nullcline in the subthreshold region is deformed once to give the burst neuron model, and a second time to give the thalamic neuron model. Each deformation can be interpreted as corresponding to the inclusion of a slow inward current in the model. As these currents are included so the associated firing properties increase in complexity.     

A hidden Markov model approach to neuron firing patterns.

Analysis and characterization of neuronal discharge patterns are of interest to neurophysiologists and neuropharmacologists. In this paper we present a hidden Markov model approach to modeling single neuron electrical activity. Basically the model assumes that each interspike interval corresponds to one of several possible states of the neuron. Fitting the model to experimental series of interspike intervals by maximum likelihood allows estimation of the number of possible underlying neuron states, the probability density functions of interspike intervals corresponding to each state, and the transition probabilities between states. We present an application to the analysis of recordings of a locus coeruleus neuron under three pharmacological conditions. The model distinguishes two states during halothane anesthesia and during recovery from halothane anesthesia, and four states after administration of clonidine. The transition probabilities yield additional insights into the mechanisms of neuron firing.     

Derivation of cable parameters for a reduced model that retains asymmetric voltage attenuation of reconstructed spinal motor neuron dendrites

Spinal motor neurons have voltage gated ion channels localized in their dendrites that generate plateau potentials. The physical separation of ion channels for spiking from plateau generating channels can result in nonlinear bistable firing patterns. The physical separation and geometry of the dendrites results in asymmetric coupling between dendrites and soma that has not been addressed in reduced models of nonlinear phenomena in motor neurons. We measured voltage attenuation properties of six anatomically reconstructed and type-identified cat spinal motor neurons to characterize asymmetric coupling between the dendrites and soma. We showed that the voltage attenuation at any distance from the soma was direction-dependent and could be described as a function of the input resistance at the soma. An analytical solution for the lumped cable parameters in a two-compartment model was derived based on this finding. This is the first two-compartment modeling approach that directly derived lumped cable parameters from the geometrical and passive electrical properties of anatomically reconstructed neurons.     

A scaling model for dichotomous branching processes

We derive an affine scaling law for dichotomous branching processes in biological systems. An application to the human bronchial tree demonstrates good agreement with experimental results.     

An equivalent cable model for neuronal trees with active membrane

A non-uniform equivalent cable model of membrane voltage changes in branching neuronal trees with active ion channels has been developed. A general branching condition is formulated, extending Rall's 3/2 power rule for passive dendritic trees so that non-uniform cable segments can be treated. The theoretical results support the use of the dendritic profile model of Clements and Redman. The theory is then applied to dendrites of different morphological type yielding qualitative different response behaviour. Received: 25 September 1997 / Accepted: 13 November 1997     

A minimal biophysical model for an excitable and oscillatory neuron

Presented here is a minimal biophysical cell model, based on work by Hodgkin and Huxley and by Rinzel, that can exhibit both excitable and oscillatory behavior. Two versions of the model are studied, which conform to data for squid and lobster giant axons.