Lobster Stomatogastric Ganglion Research Articles

Circuit dynamics in lobster stomatogastric ganglion based on winnerless competition network

The research of the circuit dynamics of the lobster stomatogastric ganglion (STG) inspires the understanding of the roles that regulates the rhythm of the lobster STG. In this paper, the lobster STG was analyzed by using Winnerless Competition (WLC) network. We got the rhythm of the gastric and pyloric circuits, which is similar to the experimental results, showing abundant firing modes especially the mixed-mode oscillations (MMOs). Firstly, we focused on the inhibitory loops in the STG which show the local dynamic properties of the system. The results show that the firing orders encode the information of these loops, and how the neurons’ spike numbers fluctuate with the change of an external stimulus acting on a neuron in the loops. Secondly, we researched the global dynamic properties of the STG. On the one hand, we discussed on the influence of the gap junction strength to firing results of the whole network. On the other hand, we showed that the WLC network of lobster STG has the potential to display chaos with a positive Lyapunov exponent.

Dynamics of transitions from anti-phase to multiple in-phase synchronizations in inhibitory coupled bursting neurons

Transition from anti-phase to in-phase synchronizations in inhibitory coupled bursting neurons is very important for locomotor rhythms of animals. Here, multiple in-phase (complete), anti-phase, and phase synchronous bursting patterns are simulated in inhibitory coupled map-based model neurons with bursting patterns, and synchronization transitions between these patterns are simulated in a two-dimensional parameter space of the coupling strength and time delay. Time delay-induced transition from anti-phase to in-phase synchronous bursting behaviors matches those observed in a biological experiment on the inhibitory coupled neurons in the stomatogastric ganglion of lobster. Furthermore, bursting patterns of the in-phase (complete) synchronous behaviors can be well interpreted with the dynamic responses of an isolated single neuron to a negative square current whose action time, duration, and strength are similar to those of the inhibitory coupling current modulated by the coupling strength and time delay. The burst of the synchronous behaviors manifests the same pattern as the square negative current-induced burst of the isolated single neuron, which is acquired with the fast–slow variable dissection method. The results not only present novel nonlinear phenomenon of the time delay-induced multiple synchronous behaviors and synchronization transitions, but also provide a reasonable interpretation with the different dynamic responses of map-based bursting model neuron to an external inhibitory stimulus applied at different phases.

Neuromodulator-evoked synaptic metaplasticity within a central pattern generator network.

Synapses show short-term activity-dependent dynamics that alter the strength of neuronal interactions. This synaptic plasticity can be tuned by neuromodulation as a form of metaplasticity. We examined neuromodulator-induced metaplasticity at a graded chemical synapse in a model central pattern generator (CPG), the pyloric network of the spiny lobster stomatogastric ganglion. Dopamine, serotonin, and octopamine each produce a unique motor pattern from the pyloric network, partially through their modulation of synaptic strength in the network. We characterized synaptic depression and its amine modulation at the graded synapse from the pyloric dilator neuron to the lateral pyloric neuron (PD→LP synapse), driving the PD neuron with both long square pulses and trains of realistic waveforms over a range of presynaptic voltages. We found that the three amines can differentially affect the amplitude of graded synaptic transmission independently of the synaptic dynamics. Low concentrations of dopamine had weak and variable effects on the strength of the graded inhibitory postsynaptic potentials (gIPSPs) but reliably accelerated the onset of synaptic depression and recovery from depression independently of gIPSP amplitude. Octopamine enhanced gIPSP amplitude but decreased the amount of synaptic depression; it slowed the onset of depression and accelerated its recovery during square pulse stimulation. Serotonin reduced gIPSP amplitude but increased the amount of synaptic depression and accelerated the onset of depression. These results suggest that amine-induced metaplasticity at graded chemical synapses can alter the parameters of synaptic dynamics in multiple and independent ways.

Conductance interaction identification by means of Boltzmann distribution and mutual information analysis in conductance-based neuron models

Recent research has focused on the causal paths that explain how neuron ionic-conductances interact to produce a particular electrophysiological behavior. Pharmacological blockage methods, neuromodulators and genetic knockouts are among the techniques used to study this issue. In this paper we propose a probabilistic approach based on the computation of the Boltzmann distribution and the mutual information of conductance interactions to learn higher-order, not necessarily pair-wise, potential co-regulation mechanisms from a database of the crustacean stomatogastric ganglion pyloric circuit models. The original database was built from experimental data obtained from lobster stomatogastric neurons [2,3]. The eight currents in the single-compartment model are based on the lobster stomatogastric ganglion neurons currents. The basic idea of our work is to assign a probability value p(x) to each neuron model depending on whether or not it satisfies a given electrophysiological property. To assign these values we use the Boltzmann probability distribution, commonly used in statistical physics to associate a probability with a system state according to its energy [4]. From the Boltzmann distribution we compute the mutual information to measure the strength of interaction between a pair of conductances at the time of producing a particular electrical activity. The particular characteristics of silent neurons were captured in the uneven distribution of bivariate marginal probabilities computed from the Boltzmann distribution. Among all conductance pairs, the highest bivariate probabilities are reached for conductance pairs (gKCa,gKd), (gNa,gKCa) and (gCaT,gCaS). With respect to previous analysis of the group of silent neurons, the correlation (gCaT,gCaS) was the only significant linear correlation identified for silent models in [1]. There were another three conductance relationships reported in [1] that fitted statistical criteria for correlations, but did not appear to have a linear relationship. They were (gH,gleak), (gKCa,gKd) and (gKCa,gCaT). All these correlations were identified as significant correlations of the computed Boltzmann distribution Our results show that probabilistic modeling based on the Boltzmann distribution can capture potential co-regulations that are not captured by the correlation analysis. The extension to capture higher-order dependencies between conductances is also straightforward. Furthermore, our results indicate that mutual information analysis allows a more detailed visualization of the structure of the conductance landscape for conductance-based neuron models.

Improving visualization and analysis of relationships between neuronal model parameters in discrete parameter spaces

We explore relationships between parameters in a 12-dimensional parameter space of a 2-compartment conductance-based model of the AB (anterior burster) neuron in the lobster stomatogastric ganglion (STG). The parameter space was created by systematically varying maximal conductances of membrane currents from a hand-tuned AB model [1] to determine ranges and variation steps that could potentially produce physiologically realistic behavior. This parameter space preparation contained between 3 to 5 discrete values for each of the 12 maximal conductances. Every parameter set representing an individual model neuron was simulated and classified as functional if it produced biologically realistic activity matching the behavior of the real AB neuron under several selection criteria [2]. Interactions between pairs of parameters in such a grid-based space can be visualized by 3D bar-plots, in which color, representing the third dimension, depicts the frequency of “good” models for each of the possible combinations of values for that pair. One can attempt to identify relationships between parameters (which, in turn, may point to a possible co-regulation of the corresponding ionic currents) by searching for “ridges” of high frequency (i.e., “hot” colors) in such plots. However, due to the limited number of parameter values, identification of such interactions in the plot, as well as any numerical analysis to follow, can be difficult. In order to deal with this limitation, and at the same time to transform the data into more of a scatter-plot-like form (which is more suitable for numerical analyses such as regression modeling or curve-fitting), random jitter is often added to the discrete values to prevent mark overlapping [3]. Although this procedure may improve the visibility of relationships between parameters under some circumstances, in extreme situations, due to its randomness, it may actually obscure interactions otherwise present in the data. Therefore, we propose two methods for increasing the clarity and interpretability of such relationships in sparse parameter spaces. The overarching idea for these methods is that the information contained in the data can be used to amplify the visualization of the relationships in the corresponding displays. We extend the random jitter technique, by utilizing the “gravity effect” and the “jump effect,” so that areas of higher density affect the results of the jitter procedure. Rather than having the original parameter values vary purely randomly within the jitter limits, the procedures, while still random, incorporate a “bias” towards the areas of higher co-occurrence. In the gravity-based approach, for each combination of the parameter values, a set of vectors proportional to the frequencies of the neighboring combinations is calculated. Then, a resultant vector is computed to “pull” the jittered points along its direction. In the jump-based approach, the jittered data points “jump” towards one of the neighboring combinations, or stay within the original jitter area, based on a probability proportional to the frequency of each of the combinations involved. In both cases, if there is a relationship present in the data, it will be enhanced, making it easier to discern and analyze. On the other hand, if there is no relationship between a given pair of parameters, the plot will be equivalent to the regular jitter. Importantly, these techniques significantly augment our previous visualization approaches [4], by allowing identification of relationships that are non-linear. For instance, the relationship between the soma delayed-rectifier potassium, IKd, and the transient calcium, ICaT, currents, which in our previous work was assumed to be linear, based on the current results, might actually have an exponential character.

Localization and function of Ih channels in a small neural network.

Subthreshold ionic currents, which activate below the firing threshold and shape the cell's firing properties, play important roles in shaping neural network activity. We examined the distribution and synaptic roles of the hyperpolarization-activated inward current (I(h)) in the pyloric network of the lobster stomatogastric ganglion (STG). I(h) channels are expressed throughout the STG in a patchy distribution and are highly expressed in the fine neuropil, an area that is rich in synaptic contacts. We performed double labeling for I(h) protein and for the presynaptic marker synaptotagmin. The large majority of labeling in the fine neuropil was adjacent but nonoverlapping, suggesting that I(h) is localized in close proximity to synapses but not in the presynaptic terminals. We compared the pattern of I(h) localization with Shal transient potassium channels, whose expression is coregulated with I(h) in many STG neurons. Unlike I(h), we found significant levels of Shal protein in the soma membrane and the primary neurite. Both proteins were found in the synaptic fine neuropil, but with little evidence of colocalization in individual neurites. We performed electrophysiological experiments to study a potential role for I(h) in regulating synaptic transmission. At a synapse between two identified pyloric neurons, the amplitude of inhibitory postsynaptic potentials (IPSPs) decreased with increasing postsynaptic activation of I(h). Pharmacological block of I(h) restored IPSP amplitudes to levels seen when I(h) was not activated. These experiments suggest that modulation of postsynaptic I(h) might play an important role in the control of synaptic strength in this rhythmogenic neural network.

Classifying functional and non-functional model neurons using the theory of rough sets

We explored a 12-dimensional parameter space of a 2-compartment model of the AB (anterior burster) neuron, which is one of the two cells that form the pacemaker kernel in the pyloric network in the lobster stomatogastric ganglion (STG). The computational exploration started with a hand-tuned AB model [1] and systematically varied maximal conductances of membrane currents to determine ranges and variation steps that could potentially produce physiologically realistic behavior. We varied the conductances for the following currents in the model: fast sodium INa and delayed-rectifier potassium IKd in the axon compartment, and delayed-rectifier IKd, calcium-dependent IKCa, transient potassium IA, transient ICaT and persistent ICaS calcium, persistent sodium INaP, and hyperpolarization-activated inward Ih in the some/neurite (S/N) compartment. To model the descending modulatory inputs, a voltage-gated inward current (such as the one activated by the neuropeptide proctolin) Iproc was added to the S/N compartment. Both compartments also contained the leak current IL. Every parameter set representing an individual model neuron was simulated and analyzed in terms of its period, burst duration, spike and slow wave amplitude, number of spikes per burst, spike frequency, and after-hyperpolarization potential, as well as the model’s responses to neuromodulator deprivation and current injections. All of the above characteristics had to be within limits determined in physiological experiments performed on the AB cell, in order for a model to be classified as functional [2]. In addition to several other data mining and visualization techniques we have previously employed to analyze the parameter space of the “good” models [3], we propose to utilize the theory of rough sets (RS) to investigate the role and importance of the parameters in differentiating between the functional and non-functional models. One of the most useful aspects of the RS theory in these kinds of classification tasks is the concept of a reduct—the smallest possible subset of attributes (i.e., maximal conductances in our model) that preserves the classification accuracy of the full set of attributes [4]. There are usually several such reducts for a given problem, and by extracting the so-called core of the reducts (i.e., the attributes that all the discovered reducts have in common), one can estimate the relative importance of the attributes. For instance, based on the 10 reducts computed from our dataset, we can state that soma CaT, NaP, Kd, KCa, and Proc, are absolutely necessary for differentiation between the functional and non-functional models (they were included in all 10 reducts), the axon Na current is very important (utilized in 9 out of 10 reducts), while the leak currents (both in the soma and the axon) seem to be the least important (they were present in 6 and 7 of the reducts, respectively). Furthermore, based on reducts, one can easily generate IF-THEN rules that not only describe how the model’s proper behavior depends on its parameters, but also how those parameters (i.e., ionic currents) “cooperate” with one another to assure such activity. For example, one of the most trustworthy rules we discovered (confidence of 78%) describes the following relationship: IF soma NaP∈[2.7μS÷6.4μS) AND soma KCa∈[3,000μS÷6,000μS) AND axon Na∈[300μS÷450μS) THEN “functional AB model,” where the values in parentheses represent the ranges for the corresponding maximum membrane conductances.

Covarying ionic conductances to emulate phase maintenance in stomatogastric neurons

Neuronal networks produce reliable functional output throughout the lifespan of an animal despite ceaseless molecular turnover and a constantly changing environment. Central pattern generators, such as that of the crustacean stomatogastric ganglion (STG), robustly maintain their functionality over a wide range of burst periods [1]. Extracellular recordings of the LP neuron of the STG have demonstrated that as the burst period varies over time, the interspike intervals change proportionally, so that the spike phases are relatively invariant. This suggests that the regulation of intraburst spike patterns is of some functional importance, but the mechanism by which the network achieves this spike phase maintenance is not clear. It has further been experimentally observed that the coregulation of ion channel conductances may play a role in governing neuronal activity [2]. The present study utilized an existing database of conductance-based model neurons [3] to determine whether the pairwise covariance of certain conductances in an STG model neuron would result in neural output that emulates the experimentally observed phenomenon of phase maintenance. By computationally covarying all pairwise combinations of the eight membrane conductances, it was seen that the positive covariance of certain conductances, including sodium conductance and transient calcium conductance, causes the model neuron to maintain a specific spike phase pattern. Results indicate the possibility of a relationship between conductance coregulation and phase maintenance in lobster STG neurons.

Multi-objective evolutionary algorithms for model neuron parameter value selection matching biological behavior under different simulation scenarios

In our previous work, we systematically explored a 12dimensional parameter space of a 2-compartment model of the AB (anterior burster) neuron, which is one of the two cells that form the pacemaker kernel in the pyloric network in the lobster stomatogastric ganglion (STG). Our computational exploration started with a hand-tuned AB model [1] and systematically varied maximal conductances of membrane currents, one at a time with all the other conductances kept constant at their hand-tuned values, to determine ranges and variation steps that produced physiologically realistic behavior. Every parameter set representing an individual model neuron in this gridbased parameter space was simulated and classified as functional if it produced biologically realistic bursting activity. Specifically, we looked at the period, burst duration, spike and slow wave amplitude, number of spikes per burst, spike frequency, and after-hyperpolarization potential, which all had to be within limits determined in physiological experiments. Furthermore, in order to be classified as good, the models had to exhibit proper responses to STG deafferentation (i.e., neuromodulator deprivation) as well as current injections both in the presence and in the absence of neuromodulation. After applying all the criteria, not only have we determined that many different parameter sets performed successfully under all tested conditions, but we have also found several very interesting relationships between parameters, which may indicate the existence of co-regulations in the investigated system [2]. However, the question has always remained: are the ranges determined by varying just one maximum conductance at a time suitable and representative of the solution space of the real AB neuron? Perhaps, if two or more conductances were varied at the same time, the ranges would have been different. Unfortunately, the answer to this question cannot be found by simply extending the ranges of parameter values to some arbitrary limits and simulating all the resulting combinations; this would be unrealistic computationally. Therefore, we utilize an approach alternative to the strict grid determination of the parameter search space, based on an application of Multi-Objective Evolutionary Algorithms (MOEA) to generate a population of neuron models meeting the above selection criteria. In MOEA, the algorithm searches for pseudo-optimal solutions by trying to optimize several, sometimes conflicting, objectives at the same time. In this case, all the selection criteria were implemented as separate objectives in the end-VEGA (elitist non-dominated Vector-Evaluated Genetic Algorithm), previously proposed by the authors [3]. The ranges of variation from the hand-tuned values were set between -100% and +400%. This allowed for an extensive and ample exploration of the parameter space. The results from the simulation of the spontaneous activity alone [4], as well as all the remaining scenarios, on the one hand confirmed our observations about the possible existence of coregulations between some currents, but on the other hand, some of the relationships seen before seem to have disappeared over the extended ranges of values. This may indicate that from Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Berlin, Germany. 18–23 July 2009

Calcium sensor parameters and readout configurations for activity-dependent homeostatic regulation of pyloric network rhythms in the lobster stomatogastric ganglion

In central pattern generating (CPG) neural networks,activity-dependent homeostatic regulation (ADHR) hasbeen proposed to explain the experimentally observedrobust activity that persists in spite of constant molecularturnover and environmental changes. In the pyloric CPGnetwork of the lobster stomatogastric ganglion (STG),ADHR is dependent on and correlated with levels of intra-cellular calcium, which acts as a second messenger thataffects ion channel and synaptic properties of the cell. Pre-vious studies showed that calcium sensors can be used tomaintain stable activity levels in individual model neu-rons [1] and pyloric rhythms in one model network [2].For regulation, these studies used deviations of the cal-cium current from a target value. However, they did notaddress the choice of sensor activation and inactivationvariables, and the robustness of selected parameters andsensor configurations in the network. To address theseissues, we developed a testbed that judges the quality of asensor by using its readings to make a prediction aboutwhether a network activity pattern is functional.To make predictions, we used a classifier trained with sen-sor readings from a model pyloric network database [3].Based on their selected activity characteristics being simi-lar to biological data, 2% of these networks were labeledas functional. In each testbed with different sensor place-ments and parameters, the percentage of functional net-works correctly predicted by the classifier is indicated witha success rate.Directly using the average calcium concentration from thethree model cells of the network resulted in a 52% predic-tion success if shuffled, establishing a control case, com-pared to 77% without shuffling. Using average calciumcurrent instead of the concentration, we obtained a simi-lar success (77%), supporting the choice by earlier cal-cium sensor models [1,2]. We confirmed that the successrate increased by the addition of activation (78%) andinactivation (86%) variables in the averaged sensors,showing that the inactivation component is indispensable(see Figure 1). By testing all combinations of selected acti-vation and inactivation parameters, we found their opti-mal values. It is biologically reasonable for the sensorminimal and maximal values to be involved in regulationand using them in addition to the sensor averagesincreased the success to 87%. Finally, using the fast, slowand DC sensors proposed earlier [1] together in the samecell marginally increased the success further to 88%.Taken together, our results suggest that activity sensing forADHR of the pyloric network can potentially be achievedwith relatively few, simple calcium sensors and that theproperties of these sensors need not necessarily beadjusted to the particular role of each neuron in the net-work.

Determining Burst Firing Time Distributions from Multiple Spike Trains

Recent experimental findings have shown the presence of robust and cell-type-specific intraburst firing patterns in bursting neurons. We address the problem of characterizing these patterns under the assumption that the bursts exhibit well-defined firing time distributions. We propose a method for estimating these distributions based on a burst alignment algorithm that minimizes the overlap among the firing time distributions of the different spikes within the burst. This method provides a good approximation to the burst's intrinsic temporal structure as a set of firing time distributions. In addition, the method allows labeling the spikes in any particular burst, establishing a correspondence between each spike and the distribution that best explains it, and identifying missing spikes. Our results on both simulated and experimental data from the lobster stomatogastric ganglion show that the proposed method provides a reliable characterization of the intraburst firing patterns and avoids the errors derived from missing spikes. This method can also be applied to nonbursting neurons as a general tool for the study and the interpretation of firing time distributions as part of a temporal neural code.

Systematic selection of model parameter values matching biological behavior under different simulation scenarios

In this work, we systematically explore a 12-dimensional parameter space of a 2-compartment model of the AB (anterior burster) neuron, which is one of the two cells that form the pacemaker kernel in the pyloric network in the lobster stomatogastric ganglion (STG). Our computational exploration started with a hand-tuned AB model [1] and systematically varied maximal conductances of membrane currents. Every parameter set for an individual model neuron was simulated and classified as functional if it produced biologically realistic bursting activity. Specifically, we were looking at the period, burst duration, spike and slow wave amplitude, number of spikes per burst, spike frequency, and after-hyperpolarization potential, which all had to be within limits determined in our physiological experiments. Furthermore, in order to be classified as good, the models had to exhibit proper responses to STG deafferentation (i.e., neuromodulator deprivation) as well as current injections both in the presence and in the absence of neuromodulation. The above selection criteria were applied in a step-by-step fashion, meaning that only the models deemed good in the previous step were tested in the next. After applying all the criteria, not only have we determined that many different parameter sets performed successfully under all tested conditions, but we have also found that application of just a subset of the selection criteria was often enough to almost fully constrain the model parameters and application of additional criteria did not significantly reduce the extent of the model's solution space. In order to further analyze this saturation effect, rather than utilizing the step-by-step approach, we have re-simulated models in our database for all testing criteria. This allowed us to draw conclusions about the relative selectiveness of particular criteria as well as to analyze overlapping regions between subsets of models that simultaneously fulfilled some, but not all, conditions. Due to this approach, we were able to pinpoint parameter values that are crucial for a proper functioning of a model under each of the simulation scenarios separately, which helps explain which maximal conductances of particular membrane currents are important for a given type of activity.

Calcium sensor properties for activity-dependent homeostatic regulation of pyloric network rhythms in the lobster stomatogastric ganglion

Event Abstract Back to Event Calcium sensor properties for activity-dependent homeostatic regulation of pyloric network rhythms in the lobster stomatogastric ganglion Cengiz Günay1*, R.M Hooper1, K.R Hammett1 and A.A Prinz1 1 University of Zagreb, United States Homeostatic regulation has been proposed as a mechanism that can explain the robust behavior of central pattern generating (CPG) neural networks observed experimentally. CPG networks, such as the pyloric network in the stomatogastric ganglion (STG) of the lobster, generate stable patterns of activity in spite of constant molecular turnover and environmental changes. Although the sensing and acting components of regulation are not yet well understood, one likely scenario is that calcium-based activity sensors drive the regulation of intrinsic cellular and synaptic properties. It has been shown that calcium can help maintain stable activity levels in individual model neurons [1], and pyloric rhythms in one network model [2]. Remaining questions are: (1) whether calcium sensors work in different network model versions, and (2) what intrinsic properties of calcium sensors are important for distinguishing functional from non-functional activity patterns. We tested an existing database of about 20 million simplified pyloric networks, constructed by varying the three lateral pyloric (LP), pyloric (PY) and anterior burster and pyloric dilator (AB/PD) neuron models and their synaptic strengths [3], to see if calcium sensors can distinguish functional pyloric activity. A set of three sensors---a fast (F), slow (S) and DC (D) sensor---in each neuron, could classify 88% of the networks correctly as functional or non-functional. To find properties necessary for these sensors, we constructed a set of 354 sensors with different activation and inactivation rates and sensitivities to calcium. We determined that there were optimal values for sensor parameters, but they were broadly tuned around these values and were independent from each other. Taken together, our results suggest that activity sensing for homeostatic regulation of the pyloric network can potentially be achieved with relatively few, simple calcium sensors and that the properties of these sensors need not necessarily be adjusted to the particular role of each neuron in the network. This work is supported by 1 R01 NS054911-01A1 from NINDS.Liu Z, Golowasch J, Marder E and Abbott LF. J. Neurosci.1998, 18(7):2309-2320

Panulirus interruptus Ih-Channel Gene PIIH: Modification of Channel Properties by Alternative Splicing and Role in Rhythmic Activity

We cloned 10 full-length variants of PIIH, the gene for I(h) from the spiny lobster, Panulirus interruptus, using reverse transcription-PCR (RT-PCR) and rapid amplification of cDNA ends (RACE). This gene shows a significant amount of alternative splicing in the S3-S4 and S4-S5 linkers, in the P-loop and the entire S6 transmembrane domain, in the cyclic nucleotide binding domain (CNBD), and near the 3' end of the gene. Functional expression of seven splice variants in Xenopus oocytes generated slowly activating hyperpolarization-activated inward currents, which were blocked by the I(h) channel blockers CsCl and ZD7288. The different splice variants had markedly varying activation kinetics and voltage dependence of activation. Bath application of 8-Br-cAMP shifted the V(1/2) to more positive potentials and accelerated the activation kinetics in an isoform-specific manner. Two variants containing a segment with an ER-retention motif in the S4-S5 loop did not produce currents in oocytes. Overexpression of one splice variant, PIIH AB(S)-I, in pyloric dilator (PD) neurons in the lobster stomatogastric ganglion produced an average threefold increase in I(h) without evoking a compensatory increase in I(A). The voltage for half-maximal activation of I(h) in PIIH AB(S)-I-expressing PDs was shifted in the depolarizing direction by 9 mV, whereas the slope factor decreased by 3.8 mV. Moreover, its activation kinetics were significantly faster than in control PDs. PIIH AB(S)-I overexpression enhanced PD neuron rhythmic firing in an amplitude-dependent manner above a minimal threshold two- to threefold increase in amplitude.

Oscillations and oscillatory behavior in small neural circuits

In order to determine the dynamical properties of central pattern generators (CPGs), we have examined the lobster stomatogastric ganglion using the tools of nonlinear dynamics. The lobster pyloric and gastric mill central pattern generators can be analyzed at both the cellular and network levels because they are small, i.e., contain only 25 neurons between them and each neuron and synapse are repeatedly identifiable from animal to animal. We discuss how the biophysical properties of each neuron and synapse in the two circuits act cooperatively to generate two different patterns of sequential activity, how these patterns are altered by neuromodulators and perturbed by noise and sensory inputs. Finally, we show how simplified Hindmarsh-Rose models can be made into analog electronic neurons that mimic the lobster neurons and in addition be incorporated into artificial CPGs with robotic applications.

Consistent dynamics suggests tight regulation of biophysical parameters in a small network of bursting neurons

The neuronal firing patterns in the pyloric network of crustaceans are remarkably consistent among animals. Although this characteristic of the pyloric network is well-known, the biophysical mechanisms underlying the regulation of the systems output are receiving renewed attention. Computer simulations of the pyloric network recently demonstrated that consistent motor output can be achieved from neurons with disparate biophysical parameters among animals. Here we address this hypothesis by pharmacologically manipulating the pyloric network and analyzing the emerging voltage oscillations and firing patterns. Our results show that the pyloric network of the lobster stomatogastric ganglion maintains consistent and regular firing patterns even when entire populations of specific voltage-gated channels and synaptic receptors are blocked. The variations of temporal parameters used to characterize the burst patterns of the neurons as well as their intraburst spike dynamics do not display statistically significant increase after blocking the transient K-currents (with 4-aminopyridine), the glutamatergic inhibitory synapses (with picrotoxin), or the cholinergic synapses (with atropine) in pyloric networks from different animals. These data suggest that in this very compact circuit, the biophysical parameters are cell-specific and tightly regulated.

Amine Modulation of Ih in a Small Neural Network

We studied the functional role and modulation of the hyperpolarization-activated inward current (I(h)) in the pyloric network of the lobster stomatogastric ganglion. In isolated neurons, I(h) is a small current with a hyperpolarized voltage of half-activation (V(Act)) and a slow time constant of activation (tau(Act)). Bath application of dopamine (DA), octopamine (OCT), or serotonin (5HT) modified I(h) in selected synaptically isolated pyloric neurons. DA significantly enhanced I(h) in the anterior burster (AB) neuron by depolarizing its V(Act), accelerating its tau(Act), and enhancing its maximal conductance (g(max)). DA more weakly enhanced I(h) in the pyloric constrictor (PY) and ventricular dilator (VD) neurons. OCT weakly depolarized V(Act) and accelerated tau(Act) in the VD and inferior cardiac (IC) neurons. 5HT depolarized V(Act) in the IC neuron. Under control conditions with intact modulatory inputs from other ganglia, the pyloric rhythm cycles strongly at about 1-2 Hz. Bath application of the I(h) blocker cesium (Cs(+)) caused a mean increase in the period of 8%, although this effect was highly variable. When Cs(+) was applied to an isolated ganglion where the pyloric rhythm had been activated only by DA, the cycle period was consistently increased by 13.5%, with no other strong changes in rhythm parameters. These results suggest that I(h) regulates the pyloric rhythm by accelerating AB pacemaker frequency, but that this effect can vary with the modulatory conditions.

Ionic Mechanism Underlying Recovery of Rhythmic Activity in Adult Isolated Neurons

Neurons exhibit long-term excitability changes necessary for maintaining proper cell and network activity in response to various inputs and perturbations. For instance, the adult crustacean pyloric network can spontaneously recover rhythmic activity after complete shutdown resulting from permanent removal of neuromodulatory inputs. Dissociated lobster stomatogastric ganglion (STG) neurons have been shown to spontaneously develop oscillatory activity via excitability changes. Rhythmic electrical stimulation can eliminate these oscillatory patterns in some cells. The ionic mechanisms underlying these changes are only partially understood. We used dissociated crab STG neurons to study the ionic mechanisms underlying spontaneous recovery of rhythmic activity and stimulation-induced activity changes. Similar to lobster neurons, rhythmic activity spontaneously develops in crab STG neurons. Rhythmic hyperpolarizing stimulation can eliminate, but more commonly accelerate, the emergence of stable oscillatory activity depending on Ca(2+) influx at hyperpolarized voltages. Our main finding is that upregulation of a Ca(2+) current and downregulation of a high-threshold K(+) current underlies the spontaneous homeostatic development of oscillatory activity. However, because of a nonlinear dependence on stimulus frequency, hyperpolarization-induced oscillations appear to be inconsistent with a homeostatic regulation of activity. We find no difference in the activity patterns or the underlying ionic currents involved between neurons of the fast pyloric and the slow gastric mill networks during the first 10 days in isolation. Dynamic-clamp experiments confirm that these conductance modifications can explain the observed activity changes. We conclude that spontaneous and stimulation-induced excitability changes in STG neurons can both result in intrinsic oscillatory activity via regulation of the same two conductances.

Identification, physiological actions, and distribution of VYRKPPFNGSIFamide (Val1‐SIFamide) in the stomatogastric nervous system of the American lobster Homarus americanus

In this study, the peptide VYRKPPFNGSIFamide (Val(1)-SIFamide) was identified in the stomatogastric nervous system (STNS) of the American lobster, Homarus americanus, using matrix-assisted laser desorption/ionization-Fourier transform mass spectrometry (MALDI-FTMS). When bath-applied to the stomatogastric ganglion (STG), synthetic Val(1)-SIFamide activated the pyloric motor pattern, increasing both burst amplitude and duration in the pyloric dilator (PD) neurons. To determine the distribution of this novel SIFamide isoform within the lobster STNS and neuroendocrine organs, a rabbit polyclonal antibody was generated against synthetic Val(1)-SIFamide. Whole-mount immunolabeling with this antibody showed that this peptide is widely distributed within the STNS, including extensive neuropil staining in the STG and commissural ganglia (CoGs) as well as immunopositive somata in the CoGs and the oesophageal ganglion. Labeling was also occasionally seen in the pericardial organ (PO), but not in the sinus gland. When present in the PO, labeling was restricted to fibers-of-passage and was never seen in release terminals. Adsorption of the antibody by either Val(1)-SIFamide or Gly(1)-SIFamide abolished all Val(1)-SIFamide staining within the STNS, including the STG neuropil, whereas adsorption by other lobster neuropeptides had no effect on immunolabeling. These data strongly suggest that the staining we report is a true reflection of the distribution of this peptide in the STNS. Collectively, our mass spectrometric, physiological, and anatomical data are consistent with Val(1)-SIFamide serving as a locally released neuromodulator in the lobster STG. Thus, our study provides the first direct demonstration of function for an SIFamide isoform in any species.

A Neural Infrastructure for Rhythmic Motor Patterns

It is possible to work out the neural circuity of many invertebrate central pattern generators (CPGs) thereby providing a basis for linking cellular processes to actual behaviors. This review summarizes the infrastructure of the two CPGs in the lobster stomatogastric ganglion in terms of circuitry, ionic conductances and chemical modulation by amines and peptides. Analysis of the circuit using modeling techniques including the use of electronic neurons closes the chapter.