Ionic currents, transmitters and models of motor pattern generators

790

Ionic currents, transmitters generators Nicholas Recent

Dale* and Frederick Kuenzi

work, combining

direct

synapses

with pharmacological

computer

simulations,

how motor

circuits

preparations, in circuit

study of ion channels manipulations

has deepened

produce

rhythmic

and

our understanding outputs.

Figure

1

and realistic of

In several

both the roles of some key ionic currents

operation

operation

and models of motor pattern

and the mechanisms

may be modulated

by which

circuit

have been identified. Partlclpating

Addresses School of Biomedical Sciences, St Andrews, *e-mail:

St Andrews,

Bute Medical Building, University Fife, Scotland KY1 6 9TS, UK

of Synapses,

[email protected]

Current

Opinion

in Neurobiology

neurons

transmitters

1997, 7:790-796

http://biomednet.com/elecref/0959436800700790 0 Current

Biology

Ltd ISSN

0959-4368

Ion channels

Abbreviations GABA

y-aminobutyric

HVA Ih

high voltage activated hyperpolarization-activated persistent inward current

IP

acid Models

cation

current

Dynamic

clamp

Current Optmon m Neurobiology

Introduction Ten years ago, the overriding goal in the search to understand rhythmic motor patterns was to define the neural circuits that comprised the underlying central pattern generator. The general strategy used by most investigators in the field is encapsulated in the top half of Figure 1. The first step was to define a behaviour and its parameters. Once this had been done, the neurons that contributed to the control of the behaviour could be identified either by correlative evidence or by cell removal (in the case of invertebrate preparations). The next step was to define a circuit diagram: the synaptic connections between the participating neurons, as well as the transmitters and receptors involved. By the late 198Os, several invertebrate and two vertebrate preparations had achieved,

to varying

degrees,

this happy

state

(see

[1,2]).

However, the terminal nature of this goal was illusory because a circuit diagram alone is insufficient to explain why a neural circuit produces a particular output. It cannot give insight into characteristics of the pattern, such as frequency, phase delays between different motor centres, the patterns of neural firing, or how the outputs may be modified by either external or intrinsic influences. The key to deeper understanding lies in explaining how the subcellular components of neurons (e.g. ion channels, synapses and dendritic structure) determine both their firing properties and, in turn, the operation of the circuit. This further analysis requires quantitative descriptions of

The steps leading towards understanding how motor circuits operate. The top half of the diagram documents the steps that have been achieved in many preparations. The bottom half (enclosed in a shaded box) summarizes the further steps that are required for a deeper, mechanistic understanding. Note that progress at this deeper level may lead to a revision of previous ideas.

these components (see shaded box in Figure 1). The experimental tools available to test the contribution of subcellular components are, however, less than ideal. For example, some tools, such as the dynamic clamp or cell removal, can only be applied to circuits composed of small numbers of neurons. Further, pharmacological manipulations of specific components are limited by the specificity of the agents involved and the possibility that the same subcellular component could be playing more than one role in different neurons. These limitations mean that computer simulations are an essential tool for trying to understand how and why neural circuits produce particular outputs. In this review, we shall consider a growing number of studies from 1995 onwards that have used a combination of voltage clamp data, pharmacological agents and computer simulations to analyze at a much deeper level the operations of neural circuits. As most of the progress has come through studying how voltage-gated currents contribute to circuit function, this will constitute the main

Currents. transmitters and models of pattern generators Dale and Kuenzi

focus

of our

review.

In the

hope

of encouraging

others

to do the same, we shall first consider the elements ‘best practice’ in such investigations before reviewing progress

that

whether studies.

any general

Strategies

has

been

made.

principles

We

shall

then

are emerging

of the

consider from

these

firmly

in mind,

neural circuits

Computer simulations of neural circuits are essential both as an interpretative aid to experimentation and as a wa) of understanding quantitatively how circuits may function. In the best cases, these simulations make predictions that can both be tested and lead to further refinement of experimental descriptions. Simulations fall into two broad categories: those that incorporate realistic models of conductances equations) and

(generally synapses,

using the and those

Hodgkin-Huxley that replace de-

tailed descriptions with simpler, more general and less physiologically based descriptions (e.g. integrate-and-fire and hlorris-Lecar equations). In general, models of the first kind are far preferable unless there are strong computational reasons for making simplifications. The predictions that come from the simplified models may be useful and interesting (e.g. [3”,4]), but they are harder to relare to specific cellular and subcellular mechanisms. The physiologically based models can be subdivided according to the way they are specified. Obviously, for a circuit composed of several types of neuron, each containing several types of ion channels and interconnected by a variety of synapses, there are many parameters that must be specified to allow a mathematical description of the circuit. There are two (non-exclusive) categories of models: those that use parameters ‘inferred’ by parameter-tuning and/or guess work, and those that use experimentally ‘determined’ parameters. This distinction is not clear cut, and all models have, to varying degrees, elements of inference and determination. Nevertheless, the distinction is useful because inferred and determined models have different uses. The process of parameter-tuning involves parameter set in a model so that its output in some specified way. This is inherently makes a number of assumptions. Firstly, must choose the data that will be used process. This will necessarily be highly

modifying the matches reality dangerous and the investigator for the tuning selected and

may not be representative or show the whole range of possible outputs. Secondly, this approach assumes that a particular property or characteristic of neural output can only be specified by a unique set of underlying parameters. However, any tuning procedure will have many apparently valid solutions, and only experimentation can allow the veracity of the competing parameter sets to be assessed. This uncertainty limits the uses of inferred models, in that predictions from such models are valid only under the (possibly incorrect) assumptions that are made in the model. With this important proviso

models

the better tool, beyond

can

be

used

to give

to a pre-existing hypothesis (e.g. models, when properly used, can

also lead to the formulation models. By contrast, the becomes, analytical

for understanding

inferred

quantitative plausibility [.5,6]). However, inferred

791

of better, more more determined

determined a model

it performs as a predictive and the realm of basic circuit operation.

This is because the number of free or tuned parameters has been restricted by experiments that are independent of the model itself. The

flow

diagram

in

Figure

2 encapsulates

what

we

consider to be the elements of best practice in the analysis of neural circuits. To start with, the analysis must rest upon a quantitative description of the available components and circuit. The description of the circuit depends upon knowing the kinetics and strengths of synaptic connections. In large vertebrate circuits, it may also require information about the densities of connections between neuronal populations. For ion channels, data taken from voltage clamp experiments are required preferably from the neurons in question. In cases where this is not possible, some investigators ‘borrow’ the parameters from other neurons or preparations to describe the channels. This is better than parameter tuning, but is still potentially misleading as channels of the same general type can display marked heterogeneity in their kinetic properties (see e.g. [7’]). The final element of description requires knowledge of the electrotonic structure of the neuron [8”,9], and the location of the synapses and channels within this structure. Although modern imaging techniques may help to provide some of this information, the details of electrotonic structure and channel location are probably the most neglected elements in current attempts to understand motor circuits. These descriptions can then be synthesized into a model, the primary purpose of which is to make predictions about the roles of the individual components and thus lead to a more fundamental understanding of the circuit. Testing specific

the predictions pharmacological

or dynamic buttresses

from agents,

the model modulators,

(by use of cell removal

clamp [lo]) not only validates the model and the emerging insights, but may also lead to

improved descriptions of the individual components or to the identification of missing components. Thus, the schema in Figure 2 is essentially iterative, with each loop generating a better descriptive base and more powerful predictions. Indeed, early formulation of a model, even when the underlying descriptions are sketchy, may help in the design of experiments to characterize the system.

Progress towards understanding circuits

neural

Encouraging and impressive progress in the quantitative analysis of the neural circuits underlying rhythmic motor patterns has been made in two invertebrate preparations (the leech heartbeat (11,12,13*,14”,15] and the crustacean

792

Motor systems

rhythm

Figure 2

1 Tests

Descriptions

Synthesis

Cell removal

generator

show

a rather

good

use

of inferred

models when much of the basic descriptive information is missing, as a result of, in this case, the complexity of the system.

Here,

the

investigators

have

used

modelling

as an exploratory tool to examine possible configurations of the respiratory circuit and to formulate predictions that can distinguish among some of the possibilities (30”).

Channel blockers Ion channels Modulatora

Understanding

Transmitter antagonists

The real circuit of the leech a combination of spike-evoked

Imaging

-

Synapses

-

Electrotonic structure

calcium

and voltage

Current Opinwn

tn Neurobdogy

The elements of ‘best’ practice that are needed to generate a mechanistic understanding of neural circuits. The elements of this fall into three phases:

tests

(experimental

stomatogascric ganglion [16,17,18*,19*]) and in a number of vertebrate preparations (e.g. swimming in Xetropus embryos [20*,21*,22,23] and lamprey [6,24-26,27*,28**], mammalian respiration [29*,30**], and trigeminal function [31*]). The common thread to this work is a quantitative analytical approach that combines at least some of the

encapsulated

in Figure

evoked or graded synaptic transmission is included in the simulated network [11,12]. This is an example of a determined model that has given insight into a possible target for modulation. Studies on both the leech heartbeat system [ 14”] and the crustacean stomatogastric ganglion [18*,19*] have shown how transmitters can modulate the frequency and phasing of output through direct and indirect actions on specific currents. Once again modelling was an essential step for quantitative understanding and assessment of the contribution of individual currents.

manipulations),

descriptions and synthesis (making models). Note that the process is iterative: predictions from models may lead to tests that require better descriptions of the underlying components. The components and pathways shown in bold illustrate the main focus of this review.

elements

heartbeat oscillator uses and graded synaptic

transmission [13’]. However, the realistic and carefully constructed model of this system can produce significantly different outputs depending upon whether the spikeSimulation

process

neuromodulation

2.

Work on the leech heartbeat system [ll,lZ], crab stomatogastric ganglion [16,17], Xenopus locomotor pattern generator [21*,23] and rat trigeminal neurons [31*] has taken a systematic approach along the lines of categorizing all the components present, creating a model and then refining it through prediction and experimental testing. Study of the stomatogastric ganglion has elegantly exploited the dynamic clamp technique to concentrate on the key currents that are the target of neuromodulators. This work shows that neuromodulators can be useful analytical tools in their own right (18’,19’]. Although they may act on several components, if these can be identified, they may shed considerable light on how the circuit operates. Work on the lamprey has borrowed parameters for channels from other preparations and used parameter-tuning [24] to produce a network simulation that can reproduce several aspects of real swimming behaviour, such as frequency, rostro-caudal phasing and entrainment [Z-5,26], and has given insight into how some neuromodulators may affect circuit operation [27’]. Investigations of the respiratory

Neural circuits are not static: both their synaptic configuration and the membrane properties of the component neurons may vary with time to allow for changes in their output within a single cycle of activity or during an episode of behaviour. For example, very elegant work with the stomatogastric ganglion shows that synaptic connections can be reconfigured within a single cycle of activity by presynaptic inhibition [32]. !vlany rhythmic motor patterns undergo run-down and self-termination in the absence of external sensory inputs, suggesting that this reflects a modulatory process intrinsic to the motor pattern generating circuits [33-371. Results from the Xenopus embryo show a hitherto unsuspected role for the purinergic transmitters ATP and adenosine [38”]. These are released during motor pattern generation, modulate voltage-gated currents and mediate a complex feedback loop that controls run-down. Finally, a growing body of evidence suggests that a variety of modulators can induce or inhibit plateau properties [39-42]. Of particular interest are those cases in which there is evidence for synaptic release of modulatory substances by neurons in the circuit [43*]. Understanding the mechanisms underlying intrinsic modulation of neural circuits cannot be achieved without direct study of the voltage-gated currents and synapses, and the use of realistic simulations.

Emerging general principles Ionic currents can be divided into three very broad categories: sustained (either no or very slow inactivation relative to the cycle period of the rhythmic activity); transient (undergo inactivation that is quicker than the cycle period of the rhythmic activity); and ion-dependent (e.g. Na+- and Ca *+-dependent K+ currents). Figure 3

Currents, transmitters and models of pattern generators Dale and Kuenzi

shows illustrate available

a schematic

of one

cycle

of rhythmic

activity

to

that these three types of current are probably at very different times in the cycle.

The sustained currents are of course available throughout and can thus play a role at any stage of the cycle: as soon as the membrane

potential

reaches

a range

793

Figure 3

Ion channel class

1

Sustained

that can activate

them, the channels will open and pass current. sustained currents will play many roles in circuit

Although operation,

one key function is to ensure initiation of the next cycle and thus maintenance of the pattern. This is not usually

Ion-dependent “m

- Locomotor cycle

I

true for the transient or ion-dependent currents. Because transient currents are inactivating, they are only available after inactivation has been removed, even if the membrane potential transient initiating

is in a range suitable for their activation. Thus, currents will mostly play important roles in transitions within the cycle following a period of

hyperpolarization (e.g. speeding or delaying burst onset). Of course, if the afterhyperpolarization following a single spike is sufficient to remove or weaken inactivation of particular channels, they would be available throughout the cycle and be an exception to this general rule. The ion-dependent currents will depend upon accumulations of the intracellular ions. Although the dynamics of ion buffering may be complex and there may be considerable variability in the kinetics and sensitivity of the ion-dependent currents to the intracellular ions, these currents are probably available predominantly towards the end of the depolarized phase of the cycle. If the currents are outward, they will play a role in terminating transitions (e.g. burst termination). By contrast, inward ion-dependent currents would help to extend the period of depolarization and could delay burst termination (e.g. [40]). Note that some ion-dependent currents build up slowly over many cycles. Specific currents seem to perform similar functions along these general lines in many instances. For example, T-type Caz+ currents and Ih contribute to post-inhibitory rebound (initiating transition) [13’,27’]; Ih and other persistent inward currents (Ip [13*] and HVA Ca2+ currents [Zl*]) help neurons to escape from synaptic inhibition and generate spikes (maintenance); whereas Caz+-dependent K+ currents may contribute to burst termination (terminating transition) [44]. Note that it is not necessarily the biggest currents that are the most powerful determinants of circuit operation: in several instances, small currents with particular kinetic properties seem to be of crucial importance [ 12,23,45]. Although modulation of synaptic strengths has always been seen as an important way of modifying circuit output, it is clear that neuromodulators also act on voltage-gated currents to change either their kinetics or their magnitude. As the operation of circuits depends critically on the precise balance of currents in the neurons, the effects of modulators on the currents offer an

Time Current Optn~on ,n Neurcholoqy

The availability

of different

the locomotor

cycle. The bottom

classes

of ion channel graph shows

varies throughout

a schematic

of how

the slow envelope of membrane potential (V,) varies throughout one locomotor cycle. Illustrated above are the availability of sustained, transient and ion-dependent channels. The transient currents are available mainly at transitions from hyperpolarized to depolarized states. The decreasing wedge represents the decreasing availability as inactivation occurs. The precise positioning and steepness of the wedge

will depend

upon the channel

kinetics

and details

of the

membrane potential during the cycle. Fast ion-dependent currents are likely to be available later in the cycle and to play a role (if they are outward) in depolarized to hyperpolarized transitions. Some slowly activating ion-dependent currents may build up gradually over many cycles (dashed line in upper panel). Once again, the details of the placing and steepness of the wedge depend upon the properties of the channels and the trajectory of membrane potential during the cycle.

important and powerful that may be independent transmission.

way of modifying circuit operation of any direct actions on synaptic

Why are neurons different? In many cases, the membrane properties of the component neurons within a motor pattern generator differ, and the operation of the circuit depends critically upon the relative strengths of the ionic conductances. This begs the question of how neurons know which channels to express and how they regulate the density of channel expression so that the membrane properties are correct. Some intriguing progress has been made toward addressing these issues in the lobster stomatogastric ganglion [7*,46*]. In general, the acquisition of the correct channel densities and the development of functional neural circuits remains largely uncharacterized. Certainly, there are feedback mechanisms that regulate channel expression: overexpression of one type may cause compensatory changes to the expression of other types [47].

Conclusions Deeper analysis of neural analytical approach that

circuits requires a reductionist is based on quantitative de-

794

Motor systems

scriptions of the subcellular components. By combining modelling with predictions and experimental tests, we can refine and deepen our understanding. Several groups have made excellent progress in this multidisciplinary approach, and general principles concerning the roles of ion channels and other subcellular components in circuit operation are beginning to emerge.

The properties of A-current (In) in stomatogastric neurons most closely resemble those of the lobster shal gene product expressed in Xenopus oocytes. In different cell types, however, the IA current density varies more than threefold. By using the single-cell reverse-transcription PCR technique, these authors established a linear correlation between the amount of shal mRNA in a cell and the IA current density. Although electrophysiology can describe the ionic currents of a cell, molecular analysis such as this is the key to understanding the structure/function relationships of the channel proteins. In addition, a whole new range of questions can be asked concerning the development, subcellular localization and regulation of ion channels in the cells of central pattern generators.

The experimental tools available to test predictions from models and to probe the workings of circuits are imperfect. A possible solution comes from the application of molecular cloning techniques [48-511. These will not only give new insight into the mechanisms controlling expression of channels and receptors, but may also allow highly specific manipulations of ion channel and receptor expression, thereby providing a better understanding of how circuits operate.

8. ..

Acknowledgements \\‘e thank the Royal Society,

the \Vcllcome rl‘rust and the Biotechnolog> and Biological Sciences Rcscarch Council (BBSRC) for generous support, and Dr Keith Sillar for valuable comments on this rcvicw.

References

and recommended

Papers of particular interest, published have been highlighted as: . l

*

Mainen ZF, Sejnowski TJ: Influence of dendritic structure on firing pattern in model neocortical neurons. Nature 1996, 382:363-366. The authors created realistic multicompartment models of cortical neurons with a common set and distribution of ion channels but with different morphologies. The dendrites lacked a fast K+ current present in the soma, but otherwise had the same currents as those present in the soma (including slow K+ currents). The authors showed that the dendritic morphology alone can influence the pattern of firing (e.g. bursting versus adapting) and that the ratio of dendritic to somatic area, as well as the strength of coupling between the dendrites and soma, was a key determinant of firing. These findings are highly relevant to the study of motor circuits in which the participating neurons may differ in their dendritic morphology, and suggest that selective modulation of conductances in the soma or dendrites could have profound effects on the firing properties of the neuron. 9.

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11.

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12.

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Weimann JM, Skiebe P, Heinzel HG, Soto C, Kopell N, JorgeRivera JC, Marder E: Modulation of oscillator interactions in the crab stomatogastric ganglion by crustacean cardioactive peptide. I Neurosci 1997, 17:1748-l 760. Roberts A, Tunstall MJ, Wolf E: Properties of networks controlling locomotion and significance of voltage dependency of NMDA channels -simulation study of rhythm generation sustained by positive feedback I Neurophysiol 1995, 73:485495. Grillner S, Deliagina T, Ekeberg 0, El Manira A, Hill RH, Lansner A, Orlovsky GN, Wall& P: Neural networks that co-ordinate locomotion and body orientation in lamprey. fiends Neurosci 1995, l&270-279. Bare DJ, Levini RM, Kim MT, Willms AR, Lanning CC, Rodriguez HE, Harris-Warrick RM: Quantitative single-cell reverse-transcription-PCR demonstrates that A-current magnitude varies as a linear function of shsl gene expression in identified stomatogastric neurons. J Neurosci 1997, 17:6597661 0.

Olsen 0H, Calabrese RL: Activation of intrinsic and synaptic currents in leech heart interneurons by realistic waveforms. j Neurosci 1996, 16:4958-4970. The slow waveform of the potentials seen in heart interneuron (HN) cells during the heartbeat rhythm was obtained by low pass filtering and then used as a voltage clamp command. This allowed the authors to look at the roles of the Cal+ currents, a persistent Na+ current and It, under conditions that would prevail during the rhythmic motor pattern and test the predictions of a previous model [l 1 ,l 21. The authors present three conclusions: first, the Ca2+ currents are significantly inactivated by these waveforms, which limits the magnitude of the graded inhibitory synaptic outputs from the HN cells; second, the persistent Na+ current was present throughout the waveforms, and alterations of its magnitude could modulate the frequency of the pattern; and third, Ih may help to stabilize the rhythm by providing corrective feedbacklonger cycle periods cause greater activation of Ih, which, in turn, allows quicker recovery from inhibition and thus speeds the rhythm. 13. .

Nadim F, Calabrese RL: A slow outward current activated by FMRFamide in heart interneurons of the medicinal leech. J Neurosci 1997, 17:4461-4472. FMRFamide accelerates the oscillatory heartbeat rhythm. However, the biophysical model Ill ,121 cannot reproduce this through the known effects of the peptide on the K+ currents. This paper characterizes better the K+ currents, and reports the discovery of a new very slowly activating outward current (IKF) that is activated by FMRFamide. The authors make two main conclusions: first, the activation and deactivation rates of IKF can influence the cycle period of the rhythm and thus could be a target for neuromodulation; and second, the incorporation of IKF into the model allows it to reproduce the effects of FMRFamide. This is a very nice example of experimentation and realistic modelling working in tandem to produce better understanding of circuit operation (cf. Figure 2). The modelling gives a quantitative understanding of the mechanism of modulation and shows how the effects of IKF may occur through changes in the activation state of other channels. 14. ..

15.

Calabrese RL, Nadim F, Olsen OH: Heartbeat control in the medicinal leech -a model system for understanding the origin, coordination, and modulation of rhythmic motor patterns. J Neurobiol 1995, 27:390-402.

16.

Golowasch J, Marder E: Ionic currents of the lateral pyloric neuron of the stomatogastric ganglion of the crab. J Neurophysiol 1992, 67:318-331.

1 7.

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Currents, transmitters and models

Harris-Warrick RM, Coniglio LM, Barazangi N, Guckenheimer J, Gueron S: Dopamine modulation of transient potassium current evokes phase-shifts in a central pattern generator network. J Neurosci 1995. 15:342-358. One effect of dopamine on PY cells in the stomatogastric ganglion is to increase their excitability on rebound from inhibition. This study determined that this effect is mediated, in part, by modulation of A-current (IA). The effects of dopamine on IA were described quantitatively and incorporated into a model of the PY cell. Although the model does not perfectly reproduce the control firing properties, changing the model parameters in accordance with the experimental results reproduced the expected changes in cell activity. This is a good example of interactive modelling in which the model both corroborated experimental findings and highlighted deficiencies in understanding. 18. .

Harris-Warrick RM, Coniglio LM, Levini RM, Gueron S, Guckenheimer J: Dopamine modulation of 2 subthreshold currents produces phase-shifts in activity of an identified motoneuron. J Neurophysiol 1995, 74:1404-l 420. Similar analysis to [IS*] for LP cells, except that dopamine modulates both IA and I,, in this case. Dopamine decreased in conductance and shifted the activation curve to more depolarized potentials. It also shifted the activation curve for Ih right and accelerated its rate of activation. In this paper, the dynamic clamp was ingeniously used to simulate experimentally the modulation of Ih in real cells. The use of the dynamic clamp and a model of the LP cell established that modulation of IA had a greater impact on cell firing than modulation of I,,. 19. .

Dale N: Kinetic characterization of the voltage-gated currents possessed by Xenopus embryo spinal neurons. I fhysiol (Land) 1995, 498:473-488. A quantitative description of the principal currents possessed by Xenopus embryo spinal neurons. Formulates the Hodgkin-Huxley models that are used in subsequent modelling studies. Of particular interest are the two components of the delayed rectifier that have speeds of activation that are two orders of magnitude different. 20. .

Dale N: Experimentally-derived model for the locomotor pattern generator in the Xenopus embryo. J Physiol fLond) 1995, 498:489-510. Utilizes the Hodgkin-Huxley models of the real conductances in Xenopus embryo spinal neurons in a highly simplified network model. This paper considers the roles of the currents in setting the membrane properties of neurons and in the operation of the swimming motor circuits. It predicts that the slow component of the delayed rectifier, although small, plays an important role in setting the frequency of repetitive firing and in motor pattern generation. The model also successfully predicts the relationship between the magnitude of reciprocal inhibition and the frequency of swimming. 21. .

22.

Wall MJ, Dale N: A slowly activating Ca2+-dependent K+ current that plays a role in termination of swimming in Xenopos embryos. J Physiol (Land) 1995, 487:557-572.

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Wallen P, Ekeberg 0, Lansner A, Brodin L, Tr%n H, Grillner S: A computer-based model for realistic simulations of neural networks. II. The segmental network generating locomotor rhythmicity in the lamprey. J Neurophysiol 1992, 68:1939-l 950.

26.

Hellgren J, Grillner S, Lansner A: Computer simulation of the segmental neural network generating locomotion in lamprey by using populations of network interneurons. Biol Cybern 1992, 68:1-l 3.

Tegner J, Hellgren-Kotaleski J, Lansner A, Grillner S: Low-voltageactivated calcium channels in the lamprey locomotor network: simulation and experiment. I Neurophysiol 1997, 77:1795-l 812. The authors borrow the parameters of the T-type current from thalamocortical neurons and then, making the assumption that T-currents are the predominant influence on rebound firing, tune the parameters so that model neurons can replicate the rebound firing exhibited by real lamprey neurons. They then perform simulations of the lamprey spinal network utilizing these new model neurons. Introduction of the T-current increases the frequency of swimming and may be importarot in allowing burst activity when the levels of excitation are low. The authors suggest that some of the modulatory action of GABA, receptors may occur through reductions of this current. 27. .

Wadden T, Hellgren J, Lansner A, Grillner S: Intersegmental coordination in the lamprey simulations using a network model without segmental boundaries. Biol Cybern 1997, 76:1-9. This paper goes beyond a common, yet possibly unrealistic, simplification often applied to spinal motor circuits: the concept of the segmental oscillator. The authors produce a large continuous network in which the neurons (described by the authors’ previous Hodgkin-Huxley models) are connected 20. ..

of pattern

generators

Dale and Kuenzi

795

to overlapping fields of followers. The patterns of connectivity are based upon what is known from the real lamprey. This new model could reproduce many aspects of the real swimming pattern as well as variants such as backward swimming (obtained by increasing the caudal excitability). However, rostrocaudal phase delays in the model, unlike in real animals, were positively correlated with the frequency of swimming. 29. .

Rybak IA, Paton JFR, Schwaber JS: Modeling neural mechanisms for genesis of respiratory rhythm and pattern. 1. Models of respiratory neurons. J Neurophysiol 1997, 77:19942006. The authors resist the temptations of parameter-tuning and instead use offthe-shelf parameters obtained for respiratory and other closely related brain stem neurons to specify a series of conductances (Na+, L- and T-type Ca*+, K(Ca), delayed rectifier). They then combine these to produce two types of neuron: one type (type I) that demonstrates spike frequency adaption (incorporates L-type but not T-type Ca 2+ channels), and a second type (type II) that demonstrates rebound firing (incorporates T-type but not L-type Ca2+ channels). The authors show that certain patterns of firing observed in real respiratory neurons can be mimicked in response to excitatory and inhibitory synaptic inputs and that these responses depend upon the strengths of the ionic conductances. 30. ..

Rybak IA, Paton JFR, Schwaber JS: Modeling neural mechanisms for genesis of respiratory rhythm and pattern. 2. Network models of the central respiratory pattern generator. J Neurophysiol 1997, 77:2007-2026. The authors use their previously described type I and type II neurons (see 129’)) and combine them in a series of possible networks (comprised of known types of respiratory neuron). In each model, different mechanisms are responsible for switching between the inspiratory and expiratory phases. The authors then explore the outputs of these model networks, each of which is capable of producing a plausible respiratory rhythm with the firing patterns resembling resptratlon. They also explore the consequences for network function of changing some of the intrinsic conductances in the neurons. The beauty of this study is that the authors are using their models as a predictive tool to explore some of the consequences of particular network architectures rather than to buttress preconceived notlons of how such a network should function. 31. .

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