The locust frontal ganglion: a central pattern generator network controlling foregut rhythmic motor patterns

The frontal ganglion (FG) is part of the insect stomatogastric nervous system and is found in most insect orders. Previous work has shown that in the desert locust, Schistocerca gregaria, the FG constitutes a major source of innervation to the foregut. In an in vitro preparation, isolated from all descending and sensory inputs, the FG spontaneously generated rhythmic multi-unit bursts of action potentials that could be recorded from all its efferent nerves. The consistent endogenous FG rhythmic pattern indicates the presence of a central pattern generator network. We found the appearance of in vitro rhythmic activity to be strongly correlated with the physiological state of the donor locust. A robust pattern emerged only after a period of saline superfusion, if the locust had a very full foregut and crop, or if the animal was close to ecdysis. Accordingly, haemolymph collected at these stages inhibited an ongoing rhythmic pattern when applied onto the ganglion. We present this novel central pattern generating system as a basis for future work on the neural network characterisation and its role in generating and controlling behaviour

Confocal analysis of nervous system architecture in direct-developing juveniles of Neanthes arenaceodentata (Annelida, Nereididae)

Background: Members of Family Nereididae have complex neural morphology exemplary of errant polychaetes and are leading research models in the investigation of annelid nervous systems. However, few studies focus on the development of their nervous system morphology. Such data are particularly relevant today, as nereidids are the subjects of a growing body of "evo-devo" work concerning bilaterian nervous systems, and detailed knowledge of their developing neuroanatomy facilitates the interpretation of gene expression analyses. In addition, new data are needed to resolve discrepancies between classic studies of nereidid neuroanatomy. We present a neuroanatomical overview based on acetylated α-tubulin labeling and confocal microscopy for post-embryonic stages of Neanthes arenaceodentata, a direct-developing nereidid. Results: At hatching (2-3 chaetigers), the nervous system has developed much of the complexity of the adult (large brain, circumesophageal connectives, nerve cords, segmental nerves), and the stomatogastric nervous system is partially formed. By the 5-chaetiger stage, the cephalic appendages and anal cirri are well innervated and have clear connections to the central nervous system. Within one week of hatching (9-chaetigers), cephalic sensory structures (e.g., nuchal organs, Langdon's organs) and brain substructures (e.g., corpora pedunculata, stomatogastric ganglia) are clearly differentiated. Additionally, the segmental-nerve architecture (including interconnections) matches descriptions of other, adult nereidids, and the pharynx has developed longitudinal nerves, nerve rings, and ganglia. All central roots of the stomatogastric nervous system are distinguishable in 12-chaetiger juveniles. Evidence was also found for two previously undescribed peripheral nerve interconnections and aspects of parapodial muscle innervation. Conclusions: N. arenaceodentata has apparently lost all essential trochophore characteristics typical of nereidids. Relative to the polychaete Capitella, brain separation from a distinct epidermis occurs later in N. arenaceodentata, indicating different mechanisms of prostomial development. Our observations of parapodial innervation and the absence of lateral nerves in N. arenaceodentata are similar to a 19th century study of Alitta virens (formerly Nereis/Neanthes virens) but contrast with a more recent study that describes a single parapodial nerve pattern and lateral nerve presence in A. virens and two other genera. The latter study apparently does not account for among-nereidid variation in these major neural features

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

Temperature sensitivity of the pyloric neuromuscular system and its modulation by dopamine

We report here the effects of temperature on the p1 neuromuscular system of the stomatogastric system of the lobster (Panulirus interruptus). Muscle force generation, in response to both the spontaneously rhythmic in vitro pyloric network neural activity and direct, controlled motor nerve stimulation, dramatically decreased as temperature increased, sufficiently that stomach movements would very unlikely be maintained at warm temperatures. However, animals fed in warm tanks showed statistically identical food digestion to those in cold tanks. Applying dopamine, a circulating hormone in crustacea, increased muscle force production at all temperatures and abolished neuromuscular system temperature dependence. Modulation may thus exist not only to increase the diversity of produced behaviors, but also to maintain individual behaviors when environmental conditions (such as temperature) vary

Training deep neural density estimators to identify mechanistic models of neural dynamics

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators-- trained using model simulations-- to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features, and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin-Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics

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

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

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

StdpC: a modern dynamic clamp

With the advancement of computer technology many novel uses of dynamic clamp have become possible. We have added new features to our dynamic clamp software StdpC (“Spike timing-dependent plasticity Clamp”) allowing such new applications while conserving the ease of use and installation of the popular earlier Dynclamp 2/4 package. Here, we introduce the new features of a waveform generator, freely programmable Hodgkin–Huxley conductances, learning synapses, graphic data displays, and a powerful scripting mechanism and discuss examples of experiments using these features. In the first example we built and ‘voltage clamped’ a conductance based model cell from a passive resistor–capacitor (RC) circuit using the dynamic clamp software to generate the voltage-dependent currents. In the second example we coupled our new spike generator through a burst detection/burst generation mechanism in a phase-dependent way to a neuron in a central pattern generator and dissected the subtle interaction between neurons, which seems to implement an information transfer through intraburst spike patterns. In the third example, making use of the new plasticity mechanism for simulated synapses, we analyzed the effect of spike timing-dependent plasticity (STDP) on synchronization revealing considerable enhancement of the entrainment of a post-synaptic neuron by a periodic spike train. These examples illustrate that with modern dynamic clamp software like StdpC, the dynamic clamp has developed beyond the mere introduction of artificial synapses or ionic conductances into neurons to a universal research tool, which might well become a standard instrument of modern electrophysiology

Synchronous Behavior of Two Coupled Electronic Neurons

We report on experimental studies of synchronization phenomena in a pair of analog electronic neurons (ENs). The ENs were designed to reproduce the observed membrane voltage oscillations of isolated biological neurons from the stomatogastric ganglion of the California spiny lobster Panulirus interruptus. The ENs are simple analog circuits which integrate four dimensional differential equations representing fast and slow subcellular mechanisms that produce the characteristic regular/chaotic spiking-bursting behavior of these cells. In this paper we study their dynamical behavior as we couple them in the same configurations as we have done for their counterpart biological neurons. The interconnections we use for these neural oscillators are both direct electrical connections and excitatory and inhibitory chemical connections: each realized by analog circuitry and suggested by biological examples. We provide here quantitative evidence that the ENs and the biological neurons behave similarly when coupled in the same manner. They each display well defined bifurcations in their mutual synchronization and regularization. We report briefly on an experiment on coupled biological neurons and four dimensional ENs which provides further ground for testing the validity of our numerical and electronic models of individual neural behavior. Our experiments as a whole present interesting new examples of regularization and synchronization in coupled nonlinear oscillators.Comment: 26 pages, 10 figure