The assembly of ionic currents in a thalamic neuron. I. The three-dimensional model

We have previously discussed qualitative models for bursting and thalamic neurons that were obtained by modifying a simple two-dimensional model for repetitive firing. In this paper we report the results of making a similar sequence of modifications to a more elaborate six-dimensional model of repetitive firing which is based on the Hodgkin-Huxley equations. To do this we first reduce the six-dimensional model to a two-dimensional model that resembles our original two-dimensional qualitative model. This is achieved by defining a new variable, which we call q. We then add a subthreshold inward current and a subthreshold outward current having a variable, z, that changes slowly. This gives a three-dimensional (v,q,z) model of the Hodgkin-Huxley type, which we refer to as the z-model. Depending on the choice of parameter values this model resembles our previous models of bursting and thalamic neurons. At each stage in the development of these models we return to the corresponding seven-dimensional model to confirm that we can obtain similar solutions by using the complete system of equations. The analysis of the three-dimensional model involves a state diagram and a stability diagram. The state diagram shows the projection of the phase path from v,q,z space into the v,z plane, together with the projections of the curves z = 0 and v = q = 0. The stability of the points on the curve v = q = 0, which we call the v, q nullcurve, is determined by the stability diagram. Taken together the state and stability diagrams show how to assemble the ionic currents to produce a given firing pattern.     

The assembly of ionic currents in a thalamic neuron. II. The stability and state diagrams

In the previous model of a thalamic neuron (R.M. Rose & J.L. Hindmarsh, Proc. R. Soc. Lond. B237, 267-288 (1989], which we referred to as the z-model, the burst response was terminated by the slow activation of a subthreshold outward current. In this paper we show that similar results can be obtained if the burst response is terminated by slow inactivation of the subthreshold inward current, Isa. We illustrate the use of this new model, which we refer to as the ha-model, by using it to explain the response of a thalamic neuron to a double ramp current. The main aim of the paper is to show how the stability and state diagrams introduced previously can be used to explain various types of firing pattern of thalamic and other neurons. We show that increasing the threshold for the fast action potentials leads to low threshold spikes of increased amplitude. Also, addition of a second subthreshold inward current adds a new stability region, which enables us to explain the origin of plateau potentials. In addition, various types of subthreshold oscillation are produced by relocating a previously stable equilibrium point in an unstable region. Finally, we predict a sequence of responses to current steps from different levels of background current that extends the burst, rest, tonic sequence of thalamic neurons. The stability and state diagrams therefore provide us with a useful way of explaining further properties of thalamic neurons and appear to have further applications to other mammalian neurons.     

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.     

Explosive route to chaos through a fractal torus in a generalized Lotka-Volterra model

The behavior of a model that generalizes the Lotka-Volterra problem into three dimensions is presented. The results show the analytic derivation of stability diagrams that describe the system's qualitative features. In particular, we show that for a certain value of the bifurcation parameter the system instantly jumps out of a steady state solution into a chaotic solution that portrays a fractal torus in the three-dimensional phase space. This scenario, is referred to as the explosive route to chaos and is attributed to the non-transversal saddle connection type bifurcation. The stability diagrams also present a region in which the Hopf type bifurcation leads to periodic and chaotic solutions. In addition, the bifurcation diagrams reveal a qualitative similarity to the data obtained in the Texas and Bordeaux experiments on the Belousov-Zhabotinskii chemical reaction. The paper is concluded by showing that the model can be useful for representing dynamics associated with biological and chemical phenomena.     

Exploring how extracellular electric field modulates neuron activity through dynamical analysis of a two-compartment neuron model

To investigate how extracellular electric field modulates neuron activity, a reduced two-compartment neuron model in the presence of electric field is introduced in this study. Depending on neuronal geometric and internal coupling parameters, the behaviors of the model have been studied extensively. The neuron model can exist in quiescent state or repetitive spiking state in response to electric field stimulus. Negative electric field mainly acts as inhibitory stimulus to the neuron, positive weak electric field could modulate spiking frequency and spike timing when the neuron is already active, and positive electric fields with sufficient intensity could directly trigger neuronal spiking in the absence of other stimulations. By bifurcation analysis, it is observed that there is saddle-node on invariant circle bifurcation, supercritical Hopf bifurcation and subcritical Hopf bifurcation appearing in the obtained two parameter bifurcation diagrams. The bifurcation structures and electric field thresholds for triggering neuron firing are determined by neuronal geometric and coupling parameters. The model predicts that the neurons with a nonsymmetric morphology between soma and dendrite, are more sensitive to electric field stimulus than those with the spherical structure. These findings suggest that neuronal geometric features play a crucial role in electric field effects on the polarization of neuronal compartments. Moreover, by determining the electric field threshold of our biophysical model, we could accurately distinguish between suprathreshold and subthreshold electric fields. Our study highlights the effects of extracellular electric field on neuronal activity from the biophysical modeling point of view. These insights into the dynamical mechanism of electric field may contribute to the investigation and development of electromagnetic therapies, and the model in our study could be further extended to a neuronal network in which the effects of electric fields on network activity may be investigated.     

Physical regulation of the self-assembly of tobacco mosaic virus coat protein

We present a statistical mechanical model based on the principle of mass action that explains the main features of the in vitro aggregation behavior of the coat protein of tobacco mosaic virus (TMV). By comparing our model to experimentally obtained stability diagrams, titration experiments, and calorimetric data, we pin down three competing factors that regulate the transitions between the different kinds of aggregated state of the coat protein. These are hydrophobic interactions, electrostatic interactions, and the formation of so-called "Caspar" carboxylate pairs. We suggest that these factors could be universal and relevant to a large class of virus coat proteins.     

The three states of globular proteins: acid denaturation.

We describe statistical mechanical theory that aims to predict protein stabilities as a function of temperature, pH, and salt concentration, from the physical properties of the constituent amino acids: (1) the number of nonpolar groups, (2) the chain length, (3) the temperature-dependent free energy of transfer, (4) the pKa's (including those in the native state) and their temperature dependencies. We calculate here the phase diagrams for apomyoglobin and hypothetical variant proteins. The theory captures essential features of protein stability including myoglobin's Tm vs pH as measured by P. L. Privalov [(1979) Advances in Protein Chemistry, Vol. 33, pp. 167-241] and its ionic strength vs pH phase diagram as measured by Y. Goto and A. L. Fink [(1990) Journal of Molecular Biology, Vol. 214, pp. 803-805]. The main predictions here are the following: (1) There are three stable states, corresponding to native (N), compact denatured (C), and highly unfolded (U), with transitions between them. (2) In agreement with experiments, the compact denatured state is predicted to have enthalpy closer to U than N because even though there is considerable hydrophobic "clustering" in C, this nevertheless represents a major loss of hydrophobic contacts relative to configurations (N) that have a hydrophobic "core." (3) C becomes more prominent in the phase diagram with increasing nonpolar content or decreasing chain length, perhaps thus accounting for (a) why lysozyme and alpha-lactalbumin differ in their denatured states, and (b) why shortened Staph nuclease molecules are compact. (4) Of major importance for protein calorimetry is Privalov's observation that the enthalpy of folding, delta H (T, pH) is independent of pH. The theory accounts for this through the prediction that the main electrostatic contribution to stability is not enthalpic; the main contribution is the entropy, mainly due to the different distributions of protons and small ions in the native and denatured states.     

Neuronal Mechanisms Encoding Global-to-Fine Information in Inferior-Temporal Cortex

Sugase et al. found that global information is represented at the initial transient firing of a single face-responsive neuron in inferior-temporal (IT) cortex, and that finer information is represented at the subsequent sustained firing. A feed-forward model and an attractor network are conceivable models to reproduce this dynamics. The attractor network, specifically an associative memory model, is employed to elucidate the neuronal mechanisms producing the dynamics. The results obtained by computer simulations show that a state of neuronal population initially approaches to a mean state of similar memory patterns, and that it finally converges to a memory pattern. This dynamics qualitatively coincides with that of face-responsive neurons. The dynamics of a single neuron in the model also coincides with that of a single face-responsive neuron. Furthermore, we propose two physiological experiments and predict the results from our model. Both predicted results are not explainable by the feed-forward model. Therefore, if the results obtained by actual physiological experiments coincide with our predicted results, the attractor network might be the neuronal mechanisms producing the dynamics of face-responsive neurons.     

Circuits constructed from identified Aplysia neurons exhibit multiple patterns of persistent activity.

We have used identified neurons from the abdominal ganglion of the mollusc Aplysia to construct and analyze two circuits in vitro. Each of these circuits was capable of producing two patterns of persistent activity; that is, they had bistable output states. The output could be switched between the stable states by a brief, external input. One circuit consisted of cocultured L10 and left upper quadrant (LUQ) neurons that formed reciprocal, inhibitory connections. In one stable state L10 was active and the LUQ was quiescent, whereas in the other stable state L10 was quiescent and the LUQ was active. A second circuit consisted of co-cultured L7 and L12 neurons that formed reciprocal, excitatory connections. In this circuit, both cells were quiescent in one stable state and both cells fired continuously in the other state. Bistable output in both circuits resulted from the nonlinear firing characteristics of each neuron and the feedback between the two neurons. We explored how the stability of the neuronal output could be controlled by the background currents injected into each neuron. We observed a relatively well-defined range of currents for which bistability occurred, consistent with the values expected from the measured strengths of the connections and a simple model. Outside of the range, the output was stable in only a single state. These results suggest how stable patterns of output are produced by some in vivo circuits and how command neurons from higher neural centers may control the activity of these circuits. The criteria that guided us in forming our circuits in culture were derived from theoretical studies on the properties of certain neuronal network models (e.g., Hopfield, J. J. 1984. Proc. Natl. Acad. Sci. USA. 81:3088-3092). Our results show that circuits consisting of only two co-cultured neurons can exhibit bistable output states of the form hypothesized to occur in populations of neurons.     

Reduction of a channel-based model for a stomatogastric ganglion LP neuron

Buchholtz et al. constructed a detailed conductance-based model of the LP cell of the stomatogastric ganglion of crustacea based upon the experimental work of Golowasch. Their model incorporated seven ionic currents and had 13 dynamical variables. We have produced a simplification of this model that has a seven-dimensional phase space by using the method of equivalent potentials, suggested by Abbott and Kepler, to combine several dynamical variables with similar time scales. Analysis of the dynamics of the reference and reduced model reveals similar bifurcation diagrams and similar dynamical behavior of the individual ionic currents.     

Analysis of bursting in a thalamic neuron model

We extend a quantitative model for low-voltage, slow-wave excitability based on the T-type calcium current (Wang et al. 1991) by juxtaposing it with a Hodgkin-Huxley-like model for fast sodium spiking in the high voltage regime to account for the distinct firing modes of thalamic neurons. We employ bifurcation analysis to illustrate the stimulus-response behavior of the full model under both voltage regimes. The model neuron shows continuous sodium spiking when depolarized sufficiently from rest. Depending on the parameters of calcium current inactivation, there are two types of low-voltage responses to a hyperpolarizing current step: a single rebound low threshold spike (LTS) upon release of the step and periodic LTSs. Bursting is seen as sodium spikes ride the LTS crest. In both cases, we analyze the LTS burst response by projecting its trajectory into a fast/slow phase plane. We also use phase plane methods to show that a potassium A-current shifts the threshold for sodium spikes, reducing the number of fast sodium spikes in an LTS burst. It can also annihilate periodic bursting. We extend the previous work of Rose and Hindmarsh (1989a–c) for a thalamic neuron and propose a simpler model for thalamic activity. We consider burst modulation by using a neuromodulator-dependent potassium leakage conductance as a control parameter. These results correspond with experiments showing that the application of certain neurotransmitters can switch firing modes. Received: 18 July 1993/Accepted in revised form: 22 January 1994     

Non-Linear Neuronal Responses as an Emergent Property of Afferent Networks: A Case Study of the Locust Lobula Giant Movement Detector

In principle it appears advantageous for single neurons to perform non-linear operations. Indeed it has been reported that some neurons show signatures of such operations in their electrophysiological response. A particular case in point is the Lobula Giant Movement Detector (LGMD) neuron of the locust, which is reported to locally perform a functional multiplication. Given the wide ramifications of this suggestion with respect to our understanding of neuronal computations, it is essential that this interpretation of the LGMD as a local multiplication unit is thoroughly tested. Here we evaluate an alternative model that tests the hypothesis that the non-linear responses of the LGMD neuron emerge from the interactions of many neurons in the opto-motor processing structure of the locust. We show, by exposing our model to standard LGMD stimulation protocols, that the properties of the LGMD that were seen as a hallmark of local non-linear operations can be explained as emerging from the dynamics of the pre-synaptic network. Moreover, we demonstrate that these properties strongly depend on the details of the synaptic projections from the medulla to the LGMD. From these observations we deduce a number of testable predictions. To assess the real-time properties of our model we applied it to a high-speed robot. These robot results show that our model of the locust opto-motor system is able to reliably stabilize the movement trajectory of the robot and can robustly support collision avoidance. In addition, these behavioural experiments suggest that the emergent non-linear responses of the LGMD neuron enhance the system''s collision detection acuity. We show how all reported properties of this neuron are consistently reproduced by this alternative model, and how they emerge from the overall opto-motor processing structure of the locust. Hence, our results propose an alternative view on neuronal computation that emphasizes the network properties as opposed to the local transformations that can be performed by single neurons.     

Protein engineering to change thermal stability for food enzymes.

In this review we have briefly indicated how the present state of knowledge allows proteins to be mutated to increase or decrease stability. We have discussed experiments on both model proteins and those of relevance to the food industry, and show how hydrophobic forces are a major driving force for folding as well as having a major role in thermostability. We have also indicated the large contribution that hydrogen bonding, electrostatic interactions and, in a less well predicted way, disulphide bridges make to thermostability.     

Stability of proteins: temperature, pressure and the role of the solvent

We focus on the various aspects of the physics related to the stability of proteins. We review the pure thermodynamic aspects of the response of a protein to pressure and temperature variations and discuss the respective stability phase diagram. We relate the experimentally observed shape of this diagram to the low degree of correlation between the fluctuations of enthalpy and volume changes associated with the folding-denaturing transition and draw attention to the fact that one order parameter is not enough to characterize the transition. We discuss in detail microscopic aspects of the various contributions to the free energy gap of proteins and put emphasis on how a cosolvent may either enlarge or diminish this gap. We review briefly the various experimental approaches to measure changes in protein stability induced by cosolvents, denaturants, but also by pressure and temperature. Finally, we discuss in detail our own molecular dynamics simulations on cytochrome c and show what happens under high pressure, how glycerol influences structure and volume fluctuations, and how all this compares with experiments.     

The stability and folding process of amyloidogenic mutant human lysozymes.

Amyloid deposits are frequently formed by mutant proteins that have a lower stability than the wild-type proteins. Some reports, however, have shown that mutant-induced thermodynamic destabilization is not always a general mechanism of amyloid formation. To obtain a better understanding of the mechanism of amyloid fibril formation, we show in this study that equilibrium and kinetic refolding-unfolding reaction experiments with two amyloidogenic mutant human lysozymes (I56T and D67H) yield folding pathways that can be drawn as Gibbs energy diagrams. The equilibrium stabilities between the native and denatured states of both mutant proteins were decreased, but the degrees of instability were different. The Gibbs energy diagrams of the folding process reveal that the Gibbs energy change between the native and folding intermediate states was similar for both proteins, and also that the activation Gibbs energy change from the native state to the transition state decreased. Our results confirm that the tendency to favor the intermediate of denaturation facilitates amyloid formation by the mutant human lysozymes more than equilibrium destabilization between the native and completely denatured states does.     

Firing patterns in a conductance-based neuron model: bifurcation, phase diagram, and chaos

Responding to various stimuli, some neurons either remain resting or can fire several distinct patterns of action potentials, such as spiking, bursting, subthreshold oscillations, and chaotic firing. In particular, Wilson’s conductance-based neocortical neuron model, derived from the Hodgkin–Huxley model, is explored to understand underlying mechanisms of the firing patterns. Phase diagrams describing boundaries between the domains of different firing patterns are obtained via extensive numerical computations. The boundaries are further studied by standard instability analyses, which demonstrates that the chaotic neural firing could develop via period-doubling and/or period- adding cascades. Sequences of the firing patterns often observed in many neural experiments are also discussed in the phase diagram framework developed. Our results lay the groundwork for wider use of the model, especially for incorporating it into neural field modeling of the brain.     

Transient responses of a modeled bursting neuron: analysis with equilibrium and averaged nullclines

We utilized a state-space approach to study the dynamics of a modeled bursting neuron consisting of 11 state variables. Such an approach may be used on a high-order system when a small number of variables are rate-limiting and dominate the dynamics of the model. Calculation of equilibrium and averaged nullclines and saddle-node bifurcations of the full and reduced models provided measures that indicated the transition between silence and spiking and the dynamics of the system during both the silent and spiking phases of the burst cycle. The relative stability of tonic beating solutions in the presence and absence of 5-HT was calculated in the state-space of the slow variables and related to specific biophysical mechanisms. The results were compared with similar simulations performed in Butera et al. (1995) which utilized a current-voltage (I-V)-based method for analysis. While the state-space method is sometimes more difficult to link to specific biophysical mechanisms, it offers a wider portrait of the dynamics of the system. In contrast, the use of I-V plots offers a direct relationship to biophysical processes, but provides no information on the dynamics of non-voltage-dependent processes such as Ca. Received: 6 December 1996 / Accepted in revised form: 1 July 1997     

Predicting the activity phase of a follower neuron with A-current in an inhibitory network

The transient potassium A-current is present in most neurons and plays an important role in determining the timing of action potentials. We examine the role of the A-current in the activity phase of a follower neuron in a rhythmic feed-forward inhibitory network with a reduced three-variable model and conduct experiments to verify the usefulness of our model. Using geometric analysis of dynamical systems, we explore the factors that determine the onset of activity in a follower neuron following release from inhibition. We first analyze the behavior of the follower neuron in a single cycle and find that the phase plane structure of the model can be used to predict the potential behaviors of the follower neuron following release from inhibition. We show that, depending on the relative scales of the inactivation time constant of the A-current and the time constant of the recovery variable, the follower neuron may or may not reach its active state following inhibition. Our simple model is used to derive a recursive set of equations to predict the contribution of the A-current parameters in determining the activity phase of a follower neuron as a function of the duration and frequency of the inhibitory input it receives. These equations can be used to demonstrate the dependence of activity phase on the period and duty cycle of the periodic inhibition, as seen by comparing the predictions of the model with the activity of the pyloric constrictor (PY) neurons in the crustacean pyloric network.     

Dynamics of the exponential integrate-and-fire model with slow currents and adaptation

In order to properly capture spike-frequency adaptation with a simplified point-neuron model, we study approximations of Hodgkin-Huxley (HH) models including slow currents by exponential integrate-and-fire (EIF) models that incorporate the same types of currents. We optimize the parameters of the EIF models under the external drive consisting of AMPA-type conductance pulses using the current-voltage curves and the van Rossum metric to best capture the subthreshold membrane potential, firing rate, and jump size of the slow current at the neuron’s spike times. Our numerical simulations demonstrate that, in addition to these quantities, the approximate EIF-type models faithfully reproduce bifurcation properties of the HH neurons with slow currents, which include spike-frequency adaptation, phase-response curves, critical exponents at the transition between a finite and infinite number of spikes with increasing constant external drive, and bifurcation diagrams of interspike intervals in time-periodically forced models. Dynamics of networks of HH neurons with slow currents can also be approximated by corresponding EIF-type networks, with the approximation being at least statistically accurate over a broad range of Poisson rates of the external drive. For the form of external drive resembling realistic, AMPA-like synaptic conductance response to incoming action potentials, the EIF model affords great savings of computation time as compared with the corresponding HH-type model. Our work shows that the EIF model with additional slow currents is well suited for use in large-scale, point-neuron models in which spike-frequency adaptation is important.     

Synchronous Clusters in a Noisy Inhibitory Neural Network

We study the stability and information encoding capacity of synchronized states in a neuronal network model that represents part of thalamic circuitry. Our model neurons have a Hodgkin-Huxley-type low-threshold calcium channel, display postinhibitory rebound, and are connected via GABAergic inhibitory synapses.We find that there is a threshold in synaptic strength, _c, below which there are no stable spiking network states. Above threshold the stable spiking state is a cluster state, where different groups of neurons fire consecutively, and each neuron fires with the same cluster each time. Weak noise destabilizes this state, but stronger noise drives the system into a different, self-organized, stochastically synchronized state. Neuronal firing is still organized in clusters, but individual neurons can hop from cluster to cluster. Noise can actually induce and sustain such a state below the threshold of synaptic strength. We do find a qualitative difference in the firing patterns between small (10 neurons) and large (1000 neurons) networks.We determine the information content of the spike trains in terms of two separate contributions: the spike-time jitter around cluster firing times, and the hopping from cluster to cluster. We quantify the information loss due to temporally correlated interspike intervals. Recent experiments on the locust olfactory system and striatal neurons suggest that the nervous system may actually use these two channels to encode separate and unique information.