Variable channel expression in identified single and electrically coupled neurons in different animals

Intrinsic biophysical diversity decorrelates neuronal firing while increasing information content

While examples of variation and diversity exist throughout the nervous system, their importance remains a source of debate. Even neurons of the same molecular type show notable intrinsic differences. Largely unknown however is the degree to which these differences impair or assist neural coding. When outputs from a single type of neuron were examined - the mitral cells of the mouse olfactory bulb - to identical stimuli, we found that each cell's spiking response was dictated by its unique biophysical fingerprint. By exploiting this intrinsic heterogeneity, diverse populations coded for 2-fold more information than their homogeneous counterparts. Additionally, biophysical variability alone reduced pairwise output spike correlations to low levels. Our results demonstrate that intrinsic neuronal diversity serves an important role in neural coding and is not simply the result of biological imprecision.

Compensation for variable intrinsic neuronal excitability by circuit-synaptic interactions

Recent theoretical and experimental work indicates that neurons tune themselves to maintain target levels of excitation by modulating ion channel expression and synaptic strengths. As a result, functionally equivalent circuits can produce similar activity despite disparate underlying network and cellular properties. To experimentally test the extent to which synaptic and intrinsic conductances can produce target activity in the presence of variability in neuronal intrinsic properties, we used the dynamic clamp to create hybrid two-cell circuits built from four types of stomatogastric neurons coupled to the same model Morris-Lecar neuron by reciprocal inhibition. We measured six intrinsic properties (input resistance, minimum membrane potential, firing rate in response to +1 nA of injected current, slope of the frequency-current curve, spike height, and spike voltage threshold) of dorsal gastric, gastric mill, lateral pyloric, and pyloric dilator neurons from male crabs of the species Cancer borealis. The intrinsic properties varied twofold to sevenfold in each cell type. We coupled each biological neuron to the Morris-Lecar model with seven different values of inhibitory synaptic conductance and also used the dynamic clamp to add seven different values of an artificial h-conductance, thus creating 49 different circuits for each biological neuron. Despite the variability in intrinsic excitability, networks formed from each neuron produced similar circuit performance at some values of synaptic and h-conductances. This work experimentally confirms results from previous modeling studies; tuning synaptic and intrinsic conductances can yield similar circuit outputs from neurons with variable intrinsic excitability.

Computational approaches to neuronal network analysis

Computational modelling is an approach to neuronal network analysis that can complement experimental approaches. Construction of useful neuron and network models is often complicated by a variety of factors and unknowns, most notably the considerable variability of cellular and synaptic properties and electrical activity characteristics found even in relatively 'simple' networks of identifiable neurons. This chapter discusses the consequences of biological variability for network modelling and analysis, describes a way to embrace variability through ensemble modelling and summarizes recent findings obtained experimentally and through ensemble modelling.

Coregulation of ion channel conductances preserves output in a computational model of a crustacean cardiac motor neuron

Similar activity patterns at both neuron and network levels can arise from different combinations of membrane and synaptic conductance values. A strategy by which neurons may preserve their electrical output is via cell type-dependent balances of inward and outward currents. Measurements of mRNA transcripts that encode ion channel proteins within motor neurons in the crustacean cardiac ganglion recently revealed correlations between certain channel types. To determine whether balances of intrinsic currents potentially resulting from such correlations preserve certain electrical cell outputs, we developed a nominal biophysical model of the crustacean cardiac ganglion using biological data. Predictions from the nominal model showed that coregulation of ionic currents may preserve the key characteristics of motor neuron activity. We then developed a methodology of sampling a multidimensional parameter space to select an appropriate model set for meaningful comparison with variations in correlations seen in biological datasets.

Conductance ratios and cellular identity

Recent experimental evidence suggests that coordinated expression of ion channels plays a role in constraining neuronal electrical activity. In particular, each neuronal cell type of the crustacean stomatogastric ganglion exhibits a unique set of positive linear correlations between ionic membrane conductances. These data suggest a causal relationship between expressed conductance correlations and features of cellular identity, namely electrical activity type. To test this idea, we used an existing database of conductance-based model neurons. We partitioned this database based on various measures of intrinsic activity, to approximate distinctions between biological cell types. We then tested individual conductance pairs for linear dependence to identify correlations. Contrary to experimental evidence, in which all conductance correlations are positive, 32% of correlations seen in this database were negative relationships. In addition, 80% of correlations seen here involved at least one calcium conductance, which have been difficult to measure experimentally. Similar to experimental results, each activity type investigated had a unique combination of correlated conductances. Finally, we found that populations of models that conform to a specific conductance correlation have a higher likelihood of exhibiting a particular feature of electrical activity. We conclude that regulating conductance ratios can support proper electrical activity of a wide range of cell types, particularly when the identity of the cell is well-defined by one or two features of its activity. Furthermore, we predict that previously unseen negative correlations and correlations involving calcium conductances are biologically plausible.

Most motor neurons receive input from the brain before transmitting to the muscle, resulting in a muscle contraction. In some cases, a small group of motor neurons can act independently to control rhythmic muscle contractions. Locomotion in mammals is thought to arise, in a large part, due to neuronal networks of this type residing in the spinal cord. However, the cellular machinery that guarantees the needed rhythmic pattern of electrical activity in these neurons is not fully understood. Here, we use a small circuit that controls stomach contractions in crustaceans like crabs and lobsters, called the pyloric circuit, to investigate potential mechanisms for regulation of neuronal activity. Ion channel proteins are integral to determination of electrical activity type. Recently, experimental studies using cells of the pyloric circuit have shown correlations in the expression of these proteins. Our study uses a mathematical model of neuronal electrical activity to detail how these correlations may be influencing activity type. We found that correlations imposed on model parameters increase the likelihood of a desired behavior, and we therefore conclude that a biological cell utilizing ion-channel correlations will have the advantage of increased robustness of activity type.

Generation and preservation of the slow underlying membrane potential oscillation in model bursting neurons

The underlying membrane potential oscillation of both forced and endogenous slow-wave bursting cells affects the number of spikes per burst, which in turn affects outputs downstream. We use a biophysical model of a class of slow-wave bursting cells with six active currents to investigate and generalize correlations among maximal current conductances that might generate and preserve its underlying oscillation. We propose three phases for the underlying oscillation for this class of cells: generation, maintenance, and termination and suggest that different current modules coregulate to preserve the characteristics of each phase. Coregulation of I(Burst) and I(A) currents within distinct boundaries maintains the dynamics during the generation phase. Similarly, coregulation of I(CaT) and I(Kd) maintains the peak and duration of the underlying oscillation, whereas the calcium-activated I(KCa) ensures appropriate termination of the oscillation and adjusts the duration independent of peak.

Mechanisms of sustained high firing rates in two classes of vestibular nucleus neurons: differential contributions of resurgent Na, Kv3, and BK currents

To fire at high rates, neurons express ionic currents that work together to minimize refractory periods by ensuring that sodium channels are available for activation shortly after each action potential. Vestibular nucleus neurons operate around high baseline firing rates and encode information with bidirectional modulation of firing rates up to several hundred Hz. To determine the mechanisms that enable these neurons to sustain firing at high rates, ionic currents were measured during firing by using the action potential clamp technique in vestibular nucleus neurons acutely dissociated from transgenic mice. Although neurons from the YFP-16 line fire at rates higher than those from the GIN line, both classes of neurons express Kv3 and BK currents as well as both transient and resurgent Na currents. In the fastest firing neurons, Kv3 currents dominated repolarization at all firing rates and minimized Na channel inactivation by rapidly transitioning Na channels from the open to the closed state. In slower firing neurons, BK currents dominated repolarization at the highest firing rates and sodium channel availability was protected by a resurgent blocking mechanism. Quantitative differences in Kv3 current density across neurons and qualitative differences in immunohistochemically detected expression of Kv3 subunits could account for the difference in firing range within and across cell classes. These results demonstrate how divergent firing properties of two neuronal populations arise through the interplay of at least three ionic currents.

Metabolic cost as a unifying principle governing neuronal biophysics

The brain contains an astonishing diversity of neurons, each expressing only one set of ion channels out of the billions of potential channel combinations. Simple organizing principles are required for us to make sense of this abundance of possibilities and wealth of related data. We suggest that energy minimization subject to functional constraints may be one such unifying principle. We compared the energy needed to produce action potentials singly and in trains for a wide range of channel densities and kinetic parameters and examined which combinations of parameters maximized spiking function while minimizing energetic cost. We confirmed these results for sodium channels using a dynamic current clamp in neocortical fast spiking interneurons. We find further evidence supporting this hypothesis in a wide range of other neurons from several species and conclude that the ion channels in these neurons minimize energy expenditure in their normal range of spiking.

A hierarchy of cell intrinsic and target-derived homeostatic signaling

Homeostatic control of neural function can be mediated by the regulation of ion channel expression, neurotransmitter receptor abundance, or modulation of presynaptic release. These processes can be implemented through cell autonomous or intercellular signaling. It remains unknown whether different forms of homeostatic regulation can be coordinated to achieve constant neural function. One way to approach this question is to confront a simple neural system with conflicting perturbations and determine whether the outcome reflects a coordinated, homeostatic response. Here, we demonstrate that two A-type potassium channel genes, shal and shaker, are reciprocally, transcriptionally coupled to maintain A-type channel expression. We then demonstrate that this homeostatic control of A-type channel expression prevents target-dependent, homeostatic modulation of synaptic transmission. Thus, we uncover a homeostatic mechanism that reciprocally regulates A-type potassium channels, and we define a hierarchical relationship between cell-intrinsic control of ion channel expression and target-derived homeostatic control of synaptic transmission.

Grouping and classifying electrophysiologically-defined classes of neocortical neurons by single cell, whole-genome expression profiling

The diversity of neuronal cell types and how to classify them are perennial questions in neuroscience. The advent of global gene expression analysis raised the possibility that comprehensive transcription profiling will resolve neuronal cell types into groups that reflect some or all aspects of their phenotype. This approach has been successfully used to compare gene expression between groups of neurons defined by a common property. Here we extend this approach to ask whether single neuron gene expression profiling can prospectively resolve neuronal subtypes into groups, independent of any phenotypic information, and whether those groups reflect meaningful biological properties of those neurons. We applied methods we have developed to compare gene expression among single neural stem cells to study global gene expression in 18 randomly picked neurons from layer II/III of the early postnatal mouse neocortex. Cells were selected by morphology and by firing characteristics and electrical properties, enabling the definition of each cell as either fast- or regular-spiking, corresponding to a class of inhibitory interneurons or excitatory pyramidal cells. Unsupervised clustering of young neurons by global gene expression resolved the cells into two groups and those broadly corresponded with the two groups of fast- and regular-spiking neurons. Clustering of the entire, diverse group of 18 neurons of different developmental stages also successfully grouped neurons in accordance with the electrophysiological phenotypes, but with more cells misassigned among groups. Genes specifically enriched in regular spiking neurons were identified from the young neuron expression dataset. These results provide a proof of principle that single-cell gene expression profiling may be used to group and classify neurons in a manner reflecting their known biological properties and may be used to identify cell-specific transcripts.

Cell-specific patterns of alternative splicing of voltage-gated ion channels in single identified neurons

CbNa(v) and CbIH encode channels that carry voltage-gated sodium and hyperpolarization activated cation currents respectively in the crab, Cancer borealis. We cloned and sequenced full length cDNAs for both CbNa(v) and CbIH and found nine different regions of alternative splicing for the CbNa(v) gene and four regions of alternative splicing for CbIH. We used RT-PCR to determine tissue-specific differences in splicing of both channel genes among cardiac muscle, skeletal muscle, brain, and stomatogastric ganglion (STG) tissue. We then examined the splice variant isoforms present in single, unambiguously identified neurons of the STG. We found cell-type specific patterns of alternative splicing for CbNa(v), indicating unique cell-specific pattern of post-transcriptional modification. Furthermore, we detected possible differences in cellular localization of alternatively spliced CbNa(v) transcripts; distinct mRNA isoforms are present between the cell somata and the axons of the neurons. In contrast, we found no qualitative differences among different cell types for CbIH variants present, although this analysis did not represent the full spectrum of all possible CbIH variants. CbIH mRNA was not detected in axon samples. Finally, although cell-type specific patterns of splicing were detected for CbNa(v), the same cell type within and between animals also displayed variability in which splice forms were detected. These results indicate that channel splicing is differentially regulated at the level of single neurons of the same neural network, providing yet another mechanism by which cell-specific neuronal output can be achieved.

Dendritic vulnerability in neurodegenerative disease: insights from analyses of cortical pyramidal neurons in transgenic mouse models

In neurodegenerative disorders, such as Alzheimer's disease, neuronal dendrites and dendritic spines undergo significant pathological changes. Because of the determinant role of these highly dynamic structures in signaling by individual neurons and ultimately in the functionality of neuronal networks that mediate cognitive functions, a detailed understanding of these changes is of paramount importance. Mutant murine models, such as the Tg2576 APP mutant mouse and the rTg4510 tau mutant mouse have been developed to provide insight into pathogenesis involving the abnormal production and aggregation of amyloid and tau proteins, because of the key role that these proteins play in neurodegenerative disease. This review showcases the multidimensional approach taken by our collaborative group to increase understanding of pathological mechanisms in neurodegenerative disease using these mouse models. This approach includes analyses of empirical 3D morphological and electrophysiological data acquired from frontal cortical pyramidal neurons using confocal laser scanning microscopy and whole-cell patch-clamp recording techniques, combined with computational modeling methodologies. These collaborative studies are designed to shed insight on the repercussions of dystrophic changes in neocortical neurons, define the cellular phenotype of differential neuronal vulnerability in relevant models of neurodegenerative disease, and provide a basis upon which to develop meaningful therapeutic strategies aimed at preventing, reversing, or compensating for neurodegenerative changes in dementia.

Model calcium sensors for network homeostasis: sensor and readout parameter analysis from a database of model neuronal networks

In activity-dependent homeostatic regulation (ADHR) of neuronal and network properties, the intracellular Ca(2+) concentration is a good candidate for sensing activity levels because it is correlated with the electrical activity of the cell. Previous ADHR models, developed with abstract activity sensors for model pyloric neurons and networks of the crustacean stomatogastric ganglion, showed that functional activity can be maintained by a regulation mechanism that senses activity levels solely from Ca(2+). At the same time, several intracellular pathways have been discovered for Ca(2+)-dependent regulation of ion channels. To generate testable predictions for dynamics of these signaling pathways, we undertook a parameter study of model Ca(2+) sensors across thousands of model pyloric networks. We found that an optimal regulation signal can be generated for 86% of model networks with a sensing mechanism that activates with a time constant of 1 ms and that inactivates within 1 s. The sensor performed robustly around this optimal point and did not need to be specific to the role of the cell. When multiple sensors with different time constants were used, coverage extended to 88% of the networks. Without changing the sensors, it extended to 95% of the networks by letting the sensors affect the readout nonlinearly. Specific to this pyloric network model, the sensor of the follower pyloric constrictor cell was more informative than the pacemaker anterior burster cell for producing a regulatory signal. Conversely, a global signal indicating network activity that was generated by summing the sensors in individual cells was less informative for regulation.

Functional consequences of animal-to-animal variation in circuit parameters

How different are the neuronal circuits for a given behavior across individual animals? To address this question, we measured multiple cellular and synaptic parameters in individual preparations to see how they correlated with circuit function, using neurons and synapses in the pyloric circuit of the stomatogastric ganglion of the crab Cancer borealis. There was considerable preparation-to-preparation variability in the strength of two identified synapses, in the amplitude of a modulator-evoked current and in the expression of six ion channel genes. Nonetheless, we found strong correlations across preparations among these parameters and attributes of circuit performance. These data illustrate the importance of making multidimensional measurements from single preparations for understanding how variability in circuit output is related to the variability of multiple circuit parameters.

Modulation of stomatogastric rhythms

Neuromodulation by peptides and amines is a primary source of plasticity in the nervous system as it adapts the animal to an ever-changing environment. The crustacean stomatogastric nervous system is one of the premier systems to study neuromodulation and its effects on motor pattern generation at the cellular level. It contains the extensively modulated central pattern generators that drive the gastric mill (chewing) and pyloric (food filtering) rhythms. Neuromodulators affect all stages of neuronal processing in this system, from membrane currents and synaptic transmission in network neurons to the properties of the effector muscles. The ease with which distinct neurons are identified and their activity is recorded in this system has provided considerable insight into the mechanisms by which neuromodulators affect their target cells and modulatory neuron function. Recent evidence suggests that neuromodulators are involved in homeostatic processes and that the modulatory system itself is under modulatory control, a fascinating topic whose surface has been barely scratched. Future challenges include exploring the behavioral conditions under which these systems are activated and how their effects are regulated.

Correlations in ion channel mRNA in rhythmically active neurons

Background

To what extent do identified neurons from different animals vary in their expression of ion channel genes? In neurons of the same type, is ion channel expression highly variable and/or is there any relationship between ion channel expression that is conserved?

Methodology/Principal Findings

To address these questions we measured ion channel mRNA in large cells (LCs) of the crab cardiac ganglion. We cloned a calcium channel, caco, and a potassium channel, shaker. Using single-cell quantitative PCR, we measured levels of mRNA for these and 6 other different ion channels in cardiac ganglion LCs. Across the population of LCs we measured 3–9 fold ranges of mRNA levels, and we found correlations in the expression of many pairs of conductances

Conclusions/Significance

In previous measurements from the crab stomatogastric ganglion (STG), ion channel expression was variable, but many pairs of channels had correlated expression. However, each STG cell type had a unique combination of ion channel correlations. Our findings from the crab cardiac ganglion are similar, but the correlations in the LCs are different from those in STG neurons, supporting the idea that such correlations could be markers of cell identity or activity.

Synaptogenesis in purified cortical subplate neurons

An ideal preparation for investigating events during synaptogenesis would be one in which synapses are sparse, but can be induced at will using a rapid, exogenous trigger. We describe a culture system of immunopurified subplate neurons in which synaptogenesis can be triggered, providing the first homogeneous culture of neocortical neurons for the investigation of synapse development. Synapses in immunopurified rat subplate neurons are sparse, and can be induced by a 48-h exposure to feeder layers of neurons and glia, an induction more rapid than any previously reported. Induced synapses are electrophysiologically functional and ultrastructurally normal. Microarray and real-time PCR experiments reveal a new program of gene expression accompanying synaptogenesis. Surprisingly few known synaptic genes are upregulated during the first 24 h of synaptogenesis; Gene Ontology annotation reveals a preferential upregulation of synaptic genes only at a later time. In situ hybridization confirms that some of the genes regulated in cultures are also expressed in the developing cortex. This culture system provides both a means of studying synapse formation in a homogeneous population of cortical neurons, and better synchronization of synaptogenesis, permitting the investigation of neuron-wide events following the triggering of synapse formation.

Reliable neuromodulation from circuits with variable underlying structure

Recent work argues that similar network performance can result from highly variable sets of network parameters, raising the question of whether neuromodulation can be reliable across individuals with networks with different sets of synaptic strengths and intrinsic membrane conductances. To address this question, we used the dynamic clamp to construct 2-cell reciprocally inhibitory networks from gastric mill (GM) neurons of the crab stomatogastric ganglion. When the strength of the artificial inhibitory synapses (g(syn)) and the conductance of an artificial I(h) (g(h)) were varied with the dynamic clamp, a variety of network behaviors resulted, including regions of stable alternating bursting. Maps of network output as a function of g(syn) and g(h) were constructed in normal saline and again in the presence of serotonin or oxotremorine. Both serotonin and oxotremorine depolarize and excite isolated individual GM neurons, but by different cellular mechanisms. Serotonin and oxotremorine each increased the size of the parameter regions that supported alternating bursting, and, on average, increased burst frequency. Nonetheless, in both cases some parameter sets within the sample space deviated from the mean population response and decreased in frequency. These data provide insight into why pharmacological treatments that work in most individuals can generate anomalous actions in a few individuals, and they have implications for understanding the evolution of nervous systems.

How multiple conductances determine electrophysiological properties in a multicompartment model

Most neurons have large numbers of voltage- and time-dependent currents that contribute to their electrical firing patterns. Because these currents are nonlinear, it can be difficult to determine the role each current plays in determining how a neuron fires. The lateral pyloric (LP) neuron of the stomatogastric ganglion of decapod crustaceans has been studied extensively biophysically. We constructed approximately 600,000 versions of a four-compartment model of the LP neuron and distributed 11 different currents into the compartments. From these, we selected approximately 1300 models that match well the electrophysiological properties of the biological neuron. Interestingly, correlations that were seen in the expression of channel mRNA in biological studies were not found across the approximately 1300 admissible LP neuron models, suggesting that the electrical phenotype does not require these correlations. We used cubic fits of the function from maximal conductances to a series of electrophysiological properties to ask which conductances predominantly influence input conductance, resting membrane potential, resting spike rate, phasing of activity in response to rhythmic inhibition, and several other properties. In all cases, multiple conductances contribute to the measured property, and the combinations of currents that strongly influence each property differ. These methods can be used to understand how multiple currents in any candidate neuron interact to determine the cell's electrophysiological behavior.

Homarus americanus stomatogastric nervous system dissection

With the goal of understanding how nervous systems produce activity and respond to the environment, neuroscientists turn to model systems that exhibit the activity of interest and are accessible and amenable to experimental methods. The stomatogastric nervous system (STNS) of the American lobster (Homarus americanus; also know was the Atlantic or Maine lobster) has been established as a model system for studying rhythm generating networks and neuromodulation of networks. The STNS consists of 3 anterior ganglia (2 commissural ganglia and an oesophageal ganglion), containing modulatory neurons that project centrally to the stomatogastric ganglion (STG). The STG contains approximately 30 neurons that comprise two central pattern generating networks, the pyloric and gastric networks that underlie feeding behaviors in crustaceans^1,2. While it is possible to study this system in vivo³, the STNS continues to produce its rhythmic activity when isolated in vitro. Physical isolation of the STNS in a dish allows for easy access to the somata in the ganglia for intracellular electrophysiological recordings and to the nerves of the STNS for extracellular recordings. Isolating the STNS is a two-part process. The first part, dissecting the stomach from the animal, is described in an accompanying video article⁴. In this video article, fine dissection techniques are used to isolate the STNS from the stomach. This procedure results in a nervous system preparation that is available for electrophysiological recordings.

Parameter sensitivity analysis in electrophysiological models using multivariable regression

Computational models of electrical activity and calcium signaling in cardiac myocytes are important tools for understanding physiology. The sensitivity of these models to changes in parameters is often not well-understood, however, because parameter evaluation can be a time-consuming, tedious process. I demonstrate here what I believe is a novel method for rapidly determining how changes in parameters affect outputs. In three models of the ventricular action potential, parameters were randomized, repeated simulations were run, important outputs were calculated, and multivariable regression was performed on the collected results. Random parameters included both maximal rates of ion transport and gating variable characteristics. The procedure generated simplified, empirical models that predicted outputs resulting from new sets of input parameters. The linear regression models were quite accurate, despite nonlinearities in the mechanistic models. Moreover, the regression coefficients, which represent parameter sensitivities, were robust, even when parameters were varied over a wide range. Most importantly, a side-by-side comparison of two similar models identified fundamental differences in model behavior, and revealed model predictions that were both consistent with, and inconsistent with, experimental data. This new method therefore shows promise as a tool for the characterization and assessment of computational models. The general strategy may also suggest methods for integrating traditional quantitative models with large-scale data sets obtained using high-throughput technologies.

A classification method to distinguish cell-specific responses elicited by current pulses in hippocampal CA1 pyramidal cells

The suprathreshold electrophysiological responses of pyramidal cells have been grouped into large classes such as bursting and spiking. However, it is not known whether, within a class, response variability ranges uniformly across all cells or whether each cell has a unique and consistent profile that can be differentiated. A major difficulty when comparing suprathreshold responses is that slight variations in spike timing in otherwise very similar traces render traditional metrics ineffective. To address these issues, we developed a novel distance measure based on fiducial points to quantify the similarity among traces with trains of action potentials and applied it together with classification techniques to a set of in vitro patch clamp recordings from CA1 pyramidal cells. We tested if responses to repeated current stimulation of a given cell would cluster together yet remain distinct from those of other cells. We found that depolarizing and hyperpolarizing current pulses elicited responses in each cell that clustered and were systematically distinguishable from responses in other cells. The fiducial-point distance measure was more effective than other methods based on spike times and voltage-gradient phase planes. Depolarizing traces were more reliably differentiated than hyperpolarizing traces, and combining both scores was even more effective. These results suggest that each CA1 pyramidal cell has unique properties that can be detected and quantified with methods discussed here. This uniqueness may be due to slight variations in morphology or membrane channel densities and kinetics, or to large, coordinated variations in these elements. Ascertaining the actual sources and their degree of variability is important when constructing network models of neural function to ensure that key mechanisms are robust in the face of variations within these ranges. The analytical tools presented here can assist in constructing detailed cell models to match experimental records to elucidate the sources of electrophysiological variability in neurons.

Probing the dynamics of identified neurons with a data-driven modeling approach

In controlling animal behavior the nervous system has to perform within the operational limits set by the requirements of each specific behavior. The implications for the corresponding range of suitable network, single neuron, and ion channel properties have remained elusive. In this article we approach the question of how well-constrained properties of neuronal systems may be on the neuronal level. We used large data sets of the activity of isolated invertebrate identified cells and built an accurate conductance-based model for this cell type using customized automated parameter estimation techniques. By direct inspection of the data we found that the variability of the neurons is larger when they are isolated from the circuit than when in the intact system. Furthermore, the responses of the neurons to perturbations appear to be more consistent than their autonomous behavior under stationary conditions. In the developed model, the constraints on different parameters that enforce appropriate model dynamics vary widely from some very tightly controlled parameters to others that are almost arbitrary. The model also allows predictions for the effect of blocking selected ionic currents and to prove that the origin of irregular dynamics in the neuron model is proper chaoticity and that this chaoticity is typical in an appropriate sense. Our results indicate that data driven models are useful tools for the in-depth analysis of neuronal dynamics. The better consistency of responses to perturbations, in the real neurons as well as in the model, suggests a paradigm shift away from measuring autonomous dynamics alone towards protocols of controlled perturbations. Our predictions for the impact of channel blockers on the neuronal dynamics and the proof of chaoticity underscore the wide scope of our approach.

Real-time kinetic modeling of voltage-gated ion channels using dynamic clamp

We propose what to our knowledge is a new technique for modeling the kinetics of voltage-gated ion channels in a functional context, in neurons or other excitable cells. The principle is to pharmacologically block the studied channel type, and to functionally replace it with dynamic clamp, on the basis of a computational model. Then, the parameters of the model are modified in real time (manually or automatically), with the objective of matching the dynamical behavior of the cell (e.g., action potential shape and spiking frequency), but also the transient and steady-state properties of the model (e.g., those derived from voltage-clamp recordings). Through this approach, one may find a model and parameter values that explain both the observed cellular dynamics and the biophysical properties of the channel. We extensively tested the method, focusing on Na(v) models. Complex Markov models (10-12 states or more) could be accurately integrated in real time at >50 kHz using the transition probability matrix, but not the explicit Euler method. The practicality of the technique was tested with experiments in raphe pacemaker neurons. Through automated real-time fitting, a Hodgkin-Huxley model could be found that reproduced well the action potential shape and the spiking frequency. Adding a virtual axonal compartment with a high density of Na(v) channels further improved the action potential shape. The computational procedure was implemented in the free QuB software, running under Microsoft Windows and featuring a friendly graphical user interface.

Dendritic excitability and synaptic plasticity

Most synaptic inputs are made onto the dendritic tree. Recent work has shown that dendrites play an active role in transforming synaptic input into neuronal output and in defining the relationships between active synapses. In this review, we discuss how these dendritic properties influence the rules governing the induction of synaptic plasticity. We argue that the location of synapses in the dendritic tree, and the type of dendritic excitability associated with each synapse, play decisive roles in determining the plastic properties of that synapse. Furthermore, since the electrical properties of the dendritic tree are not static, but can be altered by neuromodulators and by synaptic activity itself, we discuss how learning rules may be dynamically shaped by tuning dendritic function. We conclude by describing how this reciprocal relationship between plasticity of dendritic excitability and synaptic plasticity has changed our view of information processing and memory storage in neuronal networks.

Serotonin transduction cascades mediate variable changes in pyloric network cycle frequency in response to the same modulatory challenge

A fundamental question in systems biology addresses the issue of how flexibility is built into modulatory networks such that they can produce context-dependent responses. Here we examine flexibility in the serotonin (5-HT) response system that modulates the cycle frequency (cf) of a rhythmic motor output. We found that depending on the preparation, the same 5-min bath application of 5-HT to the pyloric network of the California spiny lobster, Panulirus interruptus, could produce a significant increase, decrease, or no change in steady-state cf relative to baseline. Interestingly, the mean circuit output was not significantly different among preparations prior to 5-HT application. We developed pharmacological tools to examine the preparation-to-preparation variability in the components of the 5-HT response system. We found that the 5-HT response system consisted of at least three separable components: a 5-HT(2betaPan)-like component mediated a rapid decrease followed by a sustained increase in cf; a 5-HT(1alphaPan)-like component produced a small and usually gradual increase in cf; at least one other component associated with an unknown receptor mediated a sustained decrease in cf. The magnitude of the change in cf produced by each component was highly variable, so that when summed they could produce either a net increase, decrease, or no change in cf depending on the preparation. Overall, our research demonstrates that the balance of opposing components of the 5-HT response system determines the direction and magnitude of 5-HT-induced change in steady-state cf relative to baseline.

Functional imaging, spatial reconstruction, and biophysical analysis of a respiratory motor circuit isolated in vitro

We combined real-time calcium-based neural activity imaging with whole-cell patch-clamp recording techniques to map the spatial organization and analyze electrophysiological properties of respiratory neurons forming the circuit transmitting rhythmic drive from the pre-Bötzinger complex (pre-BötC) through premotoneurons to hypoglossal (XII) motoneurons. Inspiratory pre-BötC neurons, XII premotoneurons (preMNs), and XII motoneurons (MNs) were retrogradely labeled with Ca(2+)-sensitive dye in neonatal rat in vitro brainstem slices. PreMN cell bodies were arrayed dorsomedially to pre-BötC neurons with little spatial overlap; axonal projections to MNs were ipsilateral. Inspiratory MNs were distributed in dorsal and ventral subnuclei of XII. Voltage-clamp recordings revealed that two currents, persistent sodium current (NaP) and K(+)-dominated leak current (Leak), primarily contribute to preMN/MN subthreshold current-voltage relationships. NaP or Leak conductance densities in preMNs and MNs were not significantly different. We quantified preMN and MN action potential time course and spike frequency-current (f-I) relationships and found no significant differences in repetitive spiking dynamics, steady-state f-I gains, and afterpolarizing potentials. Rhythmic synaptic drive current densities were similar in preMNs and MNs. Our results indicate that, despite topographic and morphological differences, preMNs and MNs have some common intrinsic membrane, synaptic integration, and spiking properties that we postulate ensure fidelity of inspiratory drive transmission and conversion of synaptic drive into (pre)motor output. There also appears to be a common architectonic organization for some respiratory drive transmission circuits whereby many preMNs are spatially segregated from pre-BötC rhythm-generating neurons, which we hypothesize may facilitate downstream integration of convergent inputs for premotor pattern formation.

Using complicated, wide dynamic range driving to develop models of single neurons in single recording sessions

Neuron models are typically built by measuring individually, for each membrane conductance, its parameters (e.g., half-maximal voltages) and maximal conductance value (g(max)). However, neurons have extended morphologies with nonuniform conductance distributions, whereas models generally contain at most a few compartments. Both the original conductance measurements and the models therefore unavoidably contain error due to the electrical filtering of neurons and the differential placement of conductances on them. Model parameters (typically g(max) values) are therefore generally altered by hand or brute force to match model and neuron activity. We propose an alternative method in which complicated, rapidly changing driving input is used to optimize model parameters. This method also ensures that neuron and model dynamics match across a wide dynamic range, a test not performed in most modeling. We tested this concept using leech heartbeat and generic tonically firing models and lobster stomatogastric and generic bursting models as targets and g(max) values as optimized parameters. In all four cases optimization solutions excellently matched target activity. Complicated, wide dynamic range driving thus appears to be an excellent method to characterize neuron properties in detail and to build highly accurate models. In these completely defined targets, the method found each target's 8-13 g(max) values with high accuracy, and may therefore also provide an alternative, functionally based method of defining neuron g(max) values. The method uses only standard experimental and computational techniques, could be easily extended to optimize conductance parameters other than g(max), and should be readily applicable to real neurons.

Spinal cord injury induces changes in electrophysiological properties and ion channel expression of reticulospinal neurons in larval lamprey

In larval lamprey, hemitransections were performed on the right side of the rostral spinal cord to axotomize ipsilateral reticulospinal (RS) neurons. First, at short recovery times (2-3 weeks), uninjured RS neurons contralateral to hemitransections fired a smooth train of action potentials in response to sustained depolarization, whereas axotomized neurons fired a single short burst or short repetitive bursts. For uninjured RS neurons, the afterpotentials of action potentials had three components: fast afterhyperpolarization (fAHP), afterdepolarizing potential (ADP), and slow AHP (sAHP) that was attributable to calcium influx via high-voltage-activated (HVA) (N- and P/Q-type) calcium channels and calcium-activated potassium channels (SKKCa). For axotomized RS neurons, the fAHP was significantly larger than for uninjured neurons, and the ADP and sAHP were absent or significantly reduced. Second, at relatively long recovery times (12-16 weeks), axotomized RS neurons displayed firing patterns and afterpotentials that were similar to those of uninjured neurons. Third, mRNA levels of lamprey HVA calcium and SKKCa channels in axotomized RS neurons were significantly reduced at short recovery times and restored at long recovery times. Fourth, blocking calcium channels in uninjured RS neurons resulted in altered firing patterns that resembled those produced by axotomy. We demonstrated previously that lamprey RS neurons in culture extend neurites, and calcium influx results in inhibition of neurite outgrowth or retraction. Together, these results suggest that the downregulation of Ca2+ channels in axotomized RS neurons, and the associated reduction in calcium influx, maintain intracellular calcium levels in a range that is permissive for axonal regeneration.

Artificial synaptic modification reveals a dynamical invariant in the pyloric CPG

The sequential firing of neurons in central pattern generators (CPGs) is generally thought to be a result of an interaction between intrinsic cellular and synaptic properties of the component neurons. Due to experimental limitations, it is usually difficult to address the role of each of these properties separately. We have done so by using the crustacean stomatogastric CPG and the dynamic clamp technique to measure how the network responds to the selective modification of an individual important synapse. Our results show that the burst periods and the phase lags between the constrictor (LP) and dilator (PD) neurons across preparations showed significant variability during equivalent experimental manipulations. Despite this variability, the ratio between the change in the burst period and the change in the phase lag between the same neurons was tightly preserved in all preparations, revealing a dynamical invariant in the system. This dynamical invariant was preserved despite the individual variability in the period and phase lag measurements, suggesting a tightly regulated constraint between the parameters of the network.

Neuronal firing sensitivity to morphologic and active membrane parameters

Both the excitability of a neuron's membrane, driven by active ion channels, and dendritic morphology contribute to neuronal firing dynamics, but the relative importance and interactions between these features remain poorly understood. Recent modeling studies have shown that different combinations of active conductances can evoke similar firing patterns, but have neglected how morphology might contribute to homeostasis. Parameterizing the morphology of a cylindrical dendrite, we introduce a novel application of mathematical sensitivity analysis that quantifies how dendritic length, diameter, and surface area influence neuronal firing, and compares these effects directly against those of active parameters. The method was applied to a model of neurons from goldfish Area II. These neurons exhibit, and likely contribute to, persistent activity in eye velocity storage, a simple model of working memory. We introduce sensitivity landscapes, defined by local sensitivity analyses of firing rate and gain to each parameter, performed globally across the parameter space. Principal directions over which sensitivity to all parameters varied most revealed intrinsic currents that most controlled model output. We found domains where different groups of parameters had the highest sensitivities, suggesting that interactions within each group shaped firing behaviors within each specific domain. Application of our method, and its characterization of which models were sensitive to general morphologic features, will lead to advances in understanding how realistic morphology participates in functional homeostasis. Significantly, we can predict which active conductances, and how many of them, will compensate for a given age- or development-related structural change, or will offset a morphologic perturbation resulting from trauma or neurodegenerative disorder, to restore normal function. Our method can be adapted to analyze any computational model. Thus, sensitivity landscapes, and the quantitative predictions they provide, can give new insight into mechanisms of homeostasis in any biological system.

Homeostasis is a process that allows a system to maintain a certain level of output over a long time, even though the inputs controlling the output are changing. Recently, studies of neurons and neuronal networks have shown that the “active” parameters that describe the movement of ions across the cell membrane contribute to homeostasis, since these parameters can be combined in different ways to maintain a specific output. There is also evidence that the physical shape (“morphology”) of the neuron may play a role in homeostasis, but this possibility has not been explored in computational models. We have developed a method that uses sensitivity analysis to evaluate how different kinds of parameters, like active and morphologic ones, affect model output. Across a multi-dimensional parameter space, we identified both local and global trends in parameter sensitivities that indicate regions where different parameters, even morphologic ones, contribute strongly to homeostasis. Significantly, the authors used sensitivities to predict which parameters should change, and by how much, to compensate for changes in another parameter to restore normal function. These predictions may prove important to neuronal aging, disease, and trauma research, but the method can be used to analyze any computational model.

Heterogeneous Effects of Dopamine on Highly Localized, Voltage-Induced Ca2+ Accumulation in Identified Motoneurons

Modulation of synaptic transmission is a major mechanism for the functional reconfiguration of neuronal circuits. Neurotransmitter release and, consequently, synaptic strength are regulated by intracellular Ca2+ levels in presynaptic terminals. In identified neurons of the lobster pyloric network, we studied localized, voltage-induced Ca2+ accumulation and its modulation in varicosities on distal neuritic arborizations, which have previously been shown to be sites of synaptic contacts. We previously demonstrated that dopamine (DA) weakens synaptic output from the pyloric dilator (PD) neuron and strengthens synaptic output from the lateral pyloric (LP) and pyloric constrictor (PY) neurons. Here we show that DA modifies voltage-activated Ca2+ accumulation in many varicosities in ways that are consistent with DA's effects on synaptic transmission: DA elevates Ca2+ accumulation in LP and PY varicosities and reduces Ca2+ accumulation in PD varicosities. However, in all three neuron types, we also found varicosities that were unaffected by DA. In the PY neurons, we found that DA can simultaneously increase and decrease voltage-evoked Ca2+ accumulation at different varicosities, even within the same neuron. These results suggest that regulation of Ca2+ entry is a common mechanism to regulate synaptic strength in the pyloric network. However, voltage-evoked local Ca2+ accumulation can be differentially modulated to control Ca2+-dependent processes in functionally separate varicosities of a single neuron.

A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data

We present a novel framework for automatically constraining parameters of compartmental models of neurons, given a large set of experimentally measured responses of these neurons. In experiments, intrinsic noise gives rise to a large variability (e.g., in firing pattern) in the voltage responses to repetitions of the exact same input. Thus, the common approach of fitting models by attempting to perfectly replicate, point by point, a single chosen trace out of the spectrum of variable responses does not seem to do justice to the data. In addition, finding a single error function that faithfully characterizes the distance between two spiking traces is not a trivial pursuit. To address these issues, one can adopt a multiple objective optimization approach that allows the use of several error functions jointly. When more than one error function is available, the comparison between experimental voltage traces and model response can be performed on the basis of individual features of interest (e.g., spike rate, spike width). Each feature can be compared between model and experimental mean, in units of its experimental variability, thereby incorporating into the fitting this variability. We demonstrate the success of this approach, when used in conjunction with genetic algorithm optimization, in generating an excellent fit between model behavior and the firing pattern of two distinct electrical classes of cortical interneurons, accommodating and fast-spiking. We argue that the multiple, diverse models generated by this method could serve as the building blocks for the realistic simulation of large neuronal networks.

Simple spontaneously active Hebbian learning model: homeostasis of activity and connectivity, and consequences for learning and epileptogenesis

A spontaneously active neural system that is capable of continual learning should also be capable of homeostasis of both firing rate and connectivity. Experimental evidence suggests that both types of homeostasis exist, and that connectivity is maintained at a state that is optimal for information transmission and storage. This state is referred to as the critical state. We present a simple stochastic computational Hebbian learning model that incorporates both firing rate and critical homeostasis, and we explore its stability and connectivity properties. We also examine the behavior of our model with a simulated seizure and with simulated acute deafferentation. We argue that a neural system that is more highly connected than the critical state (i.e., one that is "supercritical") is epileptogenic. Based on our simulations, we predict that the postseizural and postdeafferentation states should be supercritical and epileptogenic. Furthermore, interventions that boost spontaneous activity should be protective against epileptogenesis.

Frequency control of motor patterning by negative sensory feedback

The sensory system plays a key role in the generation of behavior by providing the nervous system with information about the environment and feedback about body movements such that motor output can continuously be adapted to changing circumstances. Although the effects of sensory organs on nervous system function have been demonstrated in many systems, the impact of sensory activity has rarely been studied in conditions in which motor output and sensory activity can interact as they do in behaving animals. In such situations, emergent properties may surface and govern the characteristics of the motor system. We studied the dynamics of sensorimotor interaction with a combination of electrophysiological experiments and computational modeling in the locust flight pattern generator, including its sensory components. The locust flight motor output is produced by a central pattern generator that interacts with phasic sensory feedback from the tegula, a proprioceptor that signals downstroke movement of the wing. We modeled the flight control system, and we tested the model predictions by replacing tegula feedback in the animal with artificial feedback through computer-controlled electric stimulation of the appropriate sensory nerves. With reference to the cycle frequency in the locust flight rhythm, our results show that motor patterns can be regulated via the variation of sensory feedback loops. In closed-loop conditions, tegula feedback strength determines cycle frequency in the model and the biological preparation such that stronger feedback results in lower frequencies. This regulatory mechanism appears to be a general emergent property of negative feedback systems.

Using constraints on neuronal activity to reveal compensatory changes in neuronal parameters

In this study, we developed a general description of parameter combinations for which specified characteristics of neuronal or network activity are constant. Our approach is based on the implicit function theorem and is applicable to activity characteristics that smoothly depend on parameters. Such smoothness is often intrinsic to neuronal systems when they are in stable functional states. The conclusions about how parameters compensate each other, developed in this study, can thus be used even without regard to the specific mathematical model describing a particular neuron or neuronal network. We showed that near a generic point in the parameter space there are infinitely many other points, or parameter combinations, for which specified characteristics of activity are the same as in the original point. These parameter combinations form a smooth manifold. This manifold can be extended as long as the gradients of characteristics are defined and independent. All possible variations of parameters compensating each other are simply all possible charts of the same manifold. The number of compensating parameters (but not parameters themselves) is fixed and equal to the number of the independent characteristics maintained. The algorithm that we developed shows how to find compensatory functional dependencies between parameters numerically. Our method can be used in the analysis of the homeostatic regulation, neuronal database search, model tuning and other applications.

Models wagging the dog: are circuits constructed with disparate parameters?

In a recent article, Prinz, Bucher, and Marder (2004) addressed the fundamental question of whether neural systems are built with a fixed blueprint of tightly controlled parameters or in a way in which properties can vary largely from one individual to another, using a database modeling approach. Here, we examine the main conclusion that neural circuits indeed are built with largely varying parameters in the light of our own experimental and modeling observations. We critically discuss the experimental and theoretical evidence, including the general adequacy of database approaches for questions of this kind, and come to the conclusion that the last word for this fundamental question has not yet been spoken.

A central pattern generator producing alternative outputs: pattern, strength, and dynamics of premotor synaptic input to leech heart motor neurons

The central pattern generator (CPG) for heartbeat in medicinal leeches consists of seven identified pairs of segmental heart interneurons and one unidentified pair. Four of the identified pairs and the unidentified pair of interneurons make inhibitory synaptic connections with segmental heart motor neurons. The CPG produces a side-to-side asymmetric pattern of intersegmental coordination among ipsilateral premotor interneurons corresponding to a similarly asymmetric fictive motor pattern in heart motor neurons, and asymmetric constriction pattern of the two tubular hearts, synchronous and peristaltic. Using extracellular recordings from premotor interneurons and voltage-clamp recordings of ipsilateral segmental motor neurons in 69 isolated nerve cords, we assessed the strength and dynamics of premotor inhibitory synaptic output onto the entire ensemble of heart motor neurons and the associated conduction delays in both coordination modes. We conclude that premotor interneurons establish a stereotypical pattern of intersegmental synaptic connectivity, strengths, and dynamics that is invariant across coordination modes, despite wide variations among preparations. These data coupled with a previous description of the temporal pattern of premotor interneuron activity and relative phasing of motor neuron activity in the two coordination modes enable a direct assessment of how premotor interneurons through their temporal pattern of activity and their spatial pattern of synaptic connectivity, strengths, and dynamics coordinate segmental motor neurons into a functional pattern of activity.

Neuromodulators, not activity, control coordinated expression of ionic currents

Electrical activity in identical neurons across individuals is often remarkably similar and stable over long periods. However, the ionic currents that determine the electrical activity of these neurons show wide animal-to-animal amplitude variability. This seemingly random variability of individual current amplitudes may obscure mechanisms that globally reduce variability and that contribute to the generation of similar neuronal output. One such mechanism could be the coordinated regulation of ionic current expression. Studying identified neurons of the Cancer borealis pyloric network, we discovered that the removal of neuromodulatory input to this network (decentralization) was accompanied by the loss of the coordinated regulation of ionic current levels. Additionally, decentralization induced large changes in the levels of several ionic currents. The loss of coregulation and the changes in current levels were prevented by continuous exogenous application of proctolin, an endogenous neuromodulatory peptide, to the pyloric network. This peptide does not exert fast regulatory actions on any of the currents affected by decentralization. We conclude that neuromodulatory inputs to the pyloric network have a novel role in the regulation of ionic current expression. They can control, over the long term, the coordinated expression of multiple voltage-gated ionic currents that they do not acutely modulate. Our results suggest that current coregulation places constraints on neuronal intrinsic plasticity and the ability of a network to respond to perturbations. The loss of conductance coregulation may be a mechanism to facilitate the recovery of function.

Quantitative expression profiling of identified neurons reveals cell-specific constraints on highly variable levels of gene expression

The postdevelopmental basis of cellular identity and the unique cellular output of a particular neuron type are of particular interest in the nervous system because a detailed understanding of circuits responsible for complex processes in the brain is impeded by the often ambiguous classification of neurons in these circuits. Neurons have been classified by morphological, electrophysiological, and neurochemical techniques. More recently, molecular approaches, particularly microarray, have been applied to the question of neuronal identity. With the realization that proteins expressed exclusively in only one type of neuron are rare, expression profiles obtained from neuronal subtypes are analyzed to search for diagnostic patterns of gene expression. However, this expression profiling hinges on one critical and implicit assumption: that neurons of the same type in different animals achieve their conserved functional output via conserved levels and quantitative relationships of gene expression. Here we exploit the unambiguously identifiable neurons in the crab stomatogastric ganglion to investigate the precise quantitative expression profiling of neurons at the level of single-cell ion channel expression. By measuring absolute mRNA levels of six different channels in the same individually identified neurons, we demonstrate that not only do individual cell types possess highly variable levels of channel expression but that this variability is constrained by unique patterns of correlated channel expression.

Convergent motor patterns from divergent circuits

Neuromodulation changes the cellular and synaptic properties of neurons, thereby enabling individual neuronal circuits to generate multiple activity patterns. However, distinct modulatory inputs could conceivably also cona different motor circuits to generate similar activity patterns. Using the isolated stomatogastric ganglion (STG) of the crab Cancer borealis, we showed previously that pyrokinin (PK) peptides activate the gastric mill (chewing) rhythm without the participation of the projection neuron modulatory commissural neuron 1 (MCN1). MCN1, which does not contain the PK peptide, also activates the gastric mill rhythm and, at these times, is a gastric mill central pattern generator (CPG) neuron. Here, we show that the gastric mill rhythms elicited by PK superfusion and MCN1 stimulation in the isolated STG are comparable, in contrast to the distinct gastric mill rhythms elicited by other input pathways. We also identified several cellular and synaptic mechanisms underlying the PK- and MCN1-elicited gastric mill rhythms that are distinct, including additional differences in their core CPG neurons. For example, the presence of the inhibitory synapse from the pyloric pacemaker neuron anterior burster onto the gastric mill CPG was necessary only for generation of the PK-elicited gastric mill rhythm. Similarly, the dorsal gastric motor neuron regulated only the PK rhythm, apparently because of PK-mediated enhancement of its synaptic actions. Thus, we demonstrate that different modulatory inputs can elicit comparable, as well as distinct activity patterns from the same neuronal ensemble. Moreover, these comparable rhythms can result from distinct CPGs using overlapping, but distinct sets of cellular and synaptic mechanisms.

Clotrimazole-sensitive K+ currents regulate pacemaker activity in interstitial cells of Cajal

Interstitial cells of Cajal (ICC) are pacemaker cells for gut peristaltic motor activity. Compared with cardiac pacemaker cells, little is known about mechanisms that regulate ICC excitability. The objective of the present study was to investigate a potential role for clotrimazole (CTL)-sensitive K currents (I(CTL)) in the regulation of ICC excitability and pacemaker activity. ICC were studied in situ and in short-term culture by using the whole cell patch-clamp configuration. In situ, ICC exhibited spontaneous transient inward currents followed by transient outward currents. CTL blocked outward currents, thereby increasing the net inward currents, and depolarized ICC, thereby establishing CTL-sensitive channels as regulators of ICC pacemaker activity. In short-term culture, a I(CTL) was identified that showed increased conductance when depolarized from the resting membrane potential to 0 mV and subsequent inward rectification at further depolarized potentials. The I(CTL) markedly increased with increasing intracellular calcium and was insensitive to the ether-à-go-go-related K channel blocker E-4031 and the large-conductance calcium-activated K channel blocker iberiotoxin. I(CTL) contributed 3-9 nS to the whole cell conductance at 0 mV membrane potential under physiological conditions; it was fast activating (tau = 88 ms), showed little time-dependent inactivation, and exhibited a deactivation time constant of 38 ms. The nitric oxide donor sodium nitroprusside (SNP) increased I(CTL). Single-channel activity, activated by calcium and SNP, was inhibited by CTL, with a single-channel conductance of approximately 38 pS. In summary, ICC generate a I(CTL) on depolarization through an intermediate-conductance calcium-activated K channel that regulates pacemaker activity and ICC excitability.

Single molecule transcription profiling with AFM

Established techniques for global gene expression profiling, such as microarrays, face fundamental sensitivity constraints. Due to greatly increasing interest in examining minute samples from micro-dissected tissues, including single cells, unorthodox approaches, including molecular nanotechnologies, are being explored in this application. Here, we examine the use of single molecule, ordered restriction mapping, combined with AFM, to measure gene transcription levels from very low abundance samples. We frame the problem mathematically, using coding theory, and present an analysis of the critical error sources that may serve as a guide to designing future studies. We follow with experiments detailing the construction of high density, single molecule, ordered restriction maps from plasmids and from cDNA molecules, using two different enzymes, a result not previously reported. We discuss these results in the context of our calculations.

Invertebrate preparations and their contribution to neurobiology in the second half of the 20th century

This review summarized the contribution to neurobiology achieved through the use of invertebrate preparations in the second half of the 20th century. This fascinating period was preceded by pioneers who explored a wide variety of invertebrate phyla and developed various preparations appropriate for electrophysiological studies. Their work advanced general knowledge about neuronal properties (dendritic, somatic, and axonal excitability; pre- and postsynaptic mechanisms). The study of invertebrates made it possible to identify cell bodies in different ganglia, and monitor their operation in the course of behavior. In the 1970s, the details of central neural circuits in worms, molluscs, insects, and crustaceans were characterized for the first time and well before equivalent findings were made in vertebrate preparations. The concept and nature of a central pattern generator (CPG) have been studied in detail, and the stomatogastric nervous system (STNS) is a fine example, having led to many major developments since it was first examined. The final part of the review is a discussion of recent neuroethological studies that have addressed simple cognitive functions and confirmed the utility of invertebrate models. After presenting our invertebrate "mice," the worm Caenorhabditis elegans and the fruit fly Drosophila melanogaster, our conclusion, based on arguments very different from those used fifty years ago, is that invertebrate models are still essential for acquiring insight into the complexity of the brain.

A Parturition-Associated Nonsynaptic Coherent Activity Pattern in the Developing Hippocampus

Correlated neuronal activity is instrumental in the formation of networks, but its emergence during maturation is poorly understood. We have used multibeam two-photon calcium microscopy combined with targeted electrophysiological recordings in order to determine the development of population coherence from embryonic to postnatal stages in the hippocampus. At embryonic stages (E16-E19), synchronized activity is absent, and neurons are intrinsically active and generate L-type channel-mediated calcium spikes. At birth, small cell assemblies coupled by gap junctions spontaneously generate synchronous nonsynaptic calcium plateaus associated to recurrent burst discharges. The emergence of coherent calcium plateaus at birth is controlled by oxytocin, a maternal hormone initiating labour, and progressively shut down a few days later by the synapse-driven giant depolarizing potentials (GDPs) that synchronize the entire network. Therefore, in the developing hippocampus, delivery is an important signal that triggers the first coherent activity pattern, which is silenced by the emergence of synaptic transmission.

Firing properties of GABAergic versus non-GABAergic vestibular nucleus neurons conferred by a differential balance of potassium currents

Neural circuits are composed of diverse cell types, the firing properties of which reflect their intrinsic ionic currents. GABAergic and non-GABAergic neurons in the medial vestibular nuclei, identified in GIN and YFP-16 lines of transgenic mice, respectively, exhibit different firing properties in brain slices. The intrinsic ionic currents of these cell types were investigated in acutely dissociated neurons from 3- to 4-wk-old mice, where differences in spontaneous firing and action potential parameters observed in slice preparations are preserved. Both GIN and YFP-16 neurons express a combination of four major outward currents: Ca(2+)-dependent K(+) currents (I(KCa)), 1 mM TEA-sensitive delayed rectifier K(+) currents (I(1TEA)), 10 mM TEA-sensitive delayed rectifier K(+) currents (I(10TEA)), and A-type K(+) currents (I(A)). The balance of these currents varied across cells, with GIN neurons tending to express proportionately more I(KCa) and I(A), and YFP-16 neurons tending to express proportionately more I(1TEA) and I(10TEA). Correlations in charge densities suggested that several currents were coregulated. Variations in the kinetics and density of I(1TEA) could account for differences in repolarization rates observed both within and between cell types. These data indicate that diversity in the firing properties of GABAergic and non-GABAergic vestibular nucleus neurons arises from graded differences in the balance and kinetics of ionic currents.

Neuronal morphology and neuropil structure in the stomatogastric ganglion of the lobster, Homarus americanus

The stomatogastric nervous system (STNS) has long been used as a model system for the study of central pattern generation, neuromodulation, and network dynamics. Anatomical studies of the crustacean stomatogastric ganglion (STG) in different species have mostly been restricted to subsets of neurons and/or general structural features. For the first time, we describe the morphology of all STG neurons belonging to the two circuits that produce the well-described pyloric and gastric rhythms in the lobster, Homarus americanus. Somata sit on the dorsal and lateral surface of the STG and send a single primary neurite into the core of the neuropil, which is mostly made up of larger lower order branches. The perimeter of the neuropil consists mostly of finer higher order branches. Immunohistochemical labeling for synaptic proteins is associated with the small diameter branches. Somata positions are not constant but show preferred locations across individuals. The number of copies is constant for all neuron types except the PY and GM neurons (PY neuron number ranges from 3 to 7, and GM neuron number ranges from 6 to 9). Branch structure is largely nondichotomous, and branches can deviate substantially from cylindrical shape. Diameter changes at branch points can be as large as 20-fold. Clearly, the morphology of a specific neuron type can be quite variable from animal to animal.