Effect of individual spiking activity on rhythm generation of central pattern generators

This is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing 58-60 (2004):10.1016/j.neucom.2004.01.091Central Pattern Generators (CPGs) are highly specialized neural networks often with redundant elements that allow the system to act properly in case of error. CPGs are multifunctional circuits, i.e. the same CPG can produce many di®erent rhythms in response to modulatory or sensory inputs. All these rhythms have to be optimal for motor control and coordination. In this paper, we use a model of the well-known pyloric CPG of crustacean to analyze the importance of redundant connections and individual spiking activity in the generation of the CPG rhythm. In particular, we study the e®ect of di®erent spike distributions of a neuron on the collective behavior of the CPG.This work was supported by the Spanish MCyT (BFI-2000- 0157 and TIC 2002-572-C02-02

Mecanismos de codificación y procesamiento de información en redes basadas en firmas neuronales

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones. Fecha de lectura: 21-02-202

Flexibility vs consistency: Quantifying differences in neuromodulatory elicited patterns of activity

Central pattern generating circuits underly fundamental behaviors such as respiration or locomotion and are under the influence of neuromodulators. The presence of neuromodulators is thought to confer flexibility to these circuits to generate distinct patterns of activity to meet distinct behavioral needs. Network output flexibility can be achieved by distinct classes of neuromodulators, those which have convergent cellular actions but divergent circuit actions or by those which have divergent cellular actions but convergent circuit actions. Both classes of neuromodulator exist in the stomatogastric nervous system of the crab Cancer borealis and influence the activity of a central pattern generating circuit in the stomatogastric ganglion, the pyloric network. The ability of both classes of neuromodulator, when applied individually, to generate qualitatively and quantitatively distinct patterns of activity has been demonstrated with respect to a baseline activity state. While it is assumed that each individual neuromodulator’s activity pattern is distinct, there has yet to be a fully quantitative description of the degree of difference between two modulated activity patterns. It is also unlikely that any single circuit will be under the influence of only a single neuromodulator at any point. Therefore, the possibility of generating distinct network outputs increases with each distinct combination of neuromodulators. While the actions of individual neuromodulators have been explored, the consequences of co-modulation on the pyloric network’s output are less understood. Previous attempts at quantifying the effects of a neuromodulator on the pyloric network output relied on evaluating only a single, often multi-dimensional, attribute of activity at a time and statistically testing the dependent parameters of that attribute with statistics that assume independence. This dissertation uses a new approach to quantify and statistically test how different one neuromodulator elicited pattern of activity is from another, preserving the inherent multi-dimensional nature of the attributes evaluated. The results of this dissertation show that the pyloric network output is able to generate statistically distinct network outputs with individual neuromodulators; however, flexibility is lost in favor of consistency under co-modulatory conditions

Thermal acclimation and habitat-dependent differences in temperature robustness of a crustacean motor circuit

Introduction: At the cellular level, acute temperature changes alter ionic conductances, ion channel kinetics, and the activity of entire neuronal circuits. This can result in severe consequences for neural function, animal behavior and survival. In poikilothermic animals, and particularly in aquatic species whose core temperature equals the surrounding water temperature, neurons experience rather rapid and wide-ranging temperature fluctuations. Recent work on pattern generating neural circuits in the crustacean stomatogastric nervous system have demonstrated that neuronal circuits can exhibit an intrinsic robustness to temperature fluctuations. However, considering the increased warming of the oceans and recurring heatwaves due to climate change, the question arises whether this intrinsic robustness can acclimate to changing environmental conditions, and whether it differs between species and ocean habitats. Methods: We address these questions using the pyloric pattern generating circuits in the stomatogastric nervous system of two crab species, Hemigrapsus sanguineus and Carcinus maenas that have seen a worldwide expansion in recent decades. Results and discussion: Consistent with their history as invasive species, we find that pyloric activity showed a broad temperature robustness (>30°C). Moreover, the temperature-robust range was dependent on habitat temperature in both species. Warm-acclimating animals shifted the critical temperature at which circuit activity breaks down to higher temperatures. This came at the cost of robustness against cold stimuli in H. sanguineus, but not in C. maenas. Comparing the temperature responses of C. maenas from a cold latitude (the North Sea) to those from a warm latitude (Spain) demonstrated that similar shifts in robustness occurred in natural environments. Our results thus demonstrate that neuronal temperature robustness correlates with, and responds to, environmental temperature conditions, potentially preparing animals for changing ecological conditions and shifting habitats

Extracting Cancer pagurus stomatogastric ganglion pyloric rhythm frequency via voltage-sensitive dye imaging data using signal processing techniques

Voltage-sensitive dye imaging (VSDI) has been widely used in the past few decades in both vertebrates and invertebrates to study, in vitro and in vivo, the nervous systems. Cancer pagurus is a seawater crab whose nervous system has a ganglion, the stomatogastric ganglion (STG) that contains a relatively small number of neurons and two rhythm forming central pattern generators (CPGs). The pyloric rhythm is one such spontaneous rhythm that can be readily observed in vitro, which makes the STG an ideal ganglion to study using VSDI. However, a major impediment to the effectiveness of VSDI is that the optically recorded data is often noisy with poor signal-to-noise ratios (SNR), rendering it difficult to study and analyse. This thesis describes the first-ever development of computational signal processing procedures that sought to extract the pyloric rhythm directly from the VSDI data, thus facilitating an accurate identification of the individual neurons in the pyloric circuit. Specifically, a multiresolution procedure based on the sequential Singular Spectrum Analysis (s-SSA) was first constructed to separate the pyloric rhythm from the noisy VSDI recording, enabling potential pyloric neurons to be detected by the presence of the pyloric frequency in the computed spectra of the respective cells. To facilitate identifying the pyloric neurons, the duty cycle (DC) was devised as a biometric, and the corresponding ratio of harmonics (RH) was determined in terms of the harmonic content of the spectrum computed for each cell/neuron as described above. Here, the instantaneous phase of the detected pyloric rhythm was also estimated, allowing it to be compared and aligned with the three distinctive pyloric phases (PD-, LP- and PY-timed) readily measured on the lateral ventricular nerve (lvn). As proof of concepts, finally, an automated method to determine the pyloric frequency directly from VSDI data was developed, over a range of SNRs, demonstrating the possibility to identify prospective pyloric neurons based on the estimated DCs and respective phase shifts measured against the analogue lvn recording

Extracting Cancer pagurus stomatogastric ganglion pyloric rhythm frequency via voltage-sensitive dye imaging data using signal processing techniques

Voltage-sensitive dye imaging (VSDI) has been widely used in the past few decades in both vertebrates and invertebrates to study, in vitro and in vivo, the nervous systems. Cancer pagurus is a seawater crab whose nervous system has a ganglion, the stomatogastric ganglion (STG) that contains a relatively small number of neurons and two rhythm forming central pattern generators (CPGs). The pyloric rhythm is one such spontaneous rhythm that can be readily observed in vitro, which makes the STG an ideal ganglion to study using VSDI. However, a major impediment to the effectiveness of VSDI is that the optically recorded data is often noisy with poor signal-to-noise ratios (SNR), rendering it difficult to study and analyse.This thesis describes the first-ever development of computational signal processing procedures that sought to extract the pyloric rhythm directly from the VSDI data, thus facilitating an accurate identification of the individual neurons in the pyloric circuit. Specifically, a multiresolution procedure based on the sequential Singular Spectrum Analysis (s-SSA) was first constructed to separate the pyloric rhythm from the noisy VSDI recording, enabling potential pyloric neurons to be detected by the presence of the pyloric frequency in the computed spectra of the respective cells. To facilitate identifying the pyloric neurons, the duty cycle (DC) was devised as a biometric, and the corresponding ratio of harmonics (RH) was determined in terms of the harmonic content of the spectrum computed for each cell/neuron as described above. Here, the instantaneous phase of the detected pyloric rhythm was also estimated, allowing it to be compared and aligned with the three distinctive pyloric phases (PD-, LP- and PY-timed) readily measured on the lateral ventricular nerve (lvn). As proof of concepts, finally, an automated method to determine the pyloric frequency directly from VSDI data was developed, over a range of SNRs, demonstrating the possibility to identify prospective pyloric neurons based on the estimated DCs and respective phase shifts measured against the analogue lvn recording

A Combinatorial Premotor Neural Code: Transformation Of Sensory Information Into Meaningful Rhythmic Motor Output By A Network Of Heterogeneous Modulatory Neurons

The goal of the following research was to investigate the contributions of neural networks in selecting distinct variants of rhythmic motor activity. We used the premotor commissural ganglion (CoG) in the stomatogastric nervous system of the Jonah crab to understand how this network effectively controls the rhythms produced in downstream motor circuits. Prior research determined that individual CoG neurons are necessary to mediate sensory-induced variation in the effected motor patterns. However, single premotor neuron inputs to the STG are not sufficient to recreate the patterns induced by the selective activation of sensory pathways. Thus, it was hypothesized that the CoG-mediated effects on these sensorimotor transformations must be explained at the level of CoG population activity. We embraced the exploratory nature of this study by approaching it in three phases. First, we established voltage-sensitive dye imaging in the stomatogastric nervous system, as a technique that reports the simultaneous activity of many neurons with single-neuron resolution. In short, this form of imaging was effective at reporting both slow and fast changes in membrane potential, and provided an effective means of staining fine neural structures through neural sheaths, structures that often act as barriers to many substances. Then, we characterized the distribution of somata in the CoG, and found that soma location was not fixed in its location from animal to animal, but that clustering of CoG somata did occur near their different nerve pathway origins. Finally, we used the voltage-sensitive dye-imaging technique to investigate the CoG population under many different sensory conditions, and found that two different sensory modalities, one chemosensory and one mechanosensory pathway, differentially affected the balance of excited and inhibited (network activation) neurons found in the CoGs. Moreover, differences in the composition of CoG participants between modalities was not extremely robust. However, it differed enough so that both CoG participation and activation were drivers of the observed changes in the downstream pyloric motor network, providing support for a premotor combinatorial code for motor pattern selection

Determining how stable network oscillations arise from neuronal and synaptic mechanisms

Many animal behaviors involve the generation of rhythmic patterns and movements. These rhythmic patterns are commonly mediated by neural networks that produce an oscillatory activity pattern, where different neurons maintain a relative phase relationship. This thesis examines the relationships between the cellular and synaptic properties that give rise to stable activity in the form of phase maintenance, across different frequencies in a well-suited model system, the pyloric network of the crab Cancer borealis. The pyloric network has endogenously oscillating ‘pacemaker’ neurons that inhibit ‘follower’ neurons, which in turn feed back onto the pacemaker neurons. The focus of this thesis was to determine the methods by which phase maintenance is achieved in an oscillatory network. This thesis examines the idea that phase maintenance occurs through the actions of intrinsic properties of isolated neurons or through the dynamics of their synaptic connections or both. A combination of pharmacological and electrophysiological techniques a used to show how identified membrane properties and short-term synaptic plasticity are involved with phase maintenance over a range of biologically relevant oscillation frequencies. To examine whether network stability is due to the characteristic stable activity of the identified pyloric neuron types, the hypothesis that phase maintenance is an inherent property of synaptically-isolated individual neurons in the pyloric network was first tested. A set of parameters were determined (frequency-dependent activity profile) to define the response of each isolated pyloric neuron to sinusoidal input at different frequencies. The parameters that define the activity profile are: burst onset phase, burst end phase, resonance frequency and intra-burst spike frequency. Each pyloric neuron type was found to possess a unique activity profile, indicating that the individual neuron types are tuned to produce a particular activity pattern at different frequencies depending on their role in the network. To elucidate the biophysical properties underlying the frequency-dependent activity profiles of the neurons, the hyperpolarization activated current (Ih) was measured and found to possess frequency-dependent properties. This implies that Ih has a different influence on the activity phase of pyloric neurons at different frequencies. Additionally, it was found that the Ih contribution to the burst onset phase depends on the neuron type: in the pacemaker group neurons (PD) it had no influence on the burst onset phase at any frequency whereas in follower neurons it acted to advance the onset phase in one neuron type (LP) and, paradoxically, to delay it in a different neuron type (PY). The results from this part of the study provided evidence that stability is due in part to the intrinsic neuronal properties but that these intrinsic properties do not fully explain network stability. To address the contribution of pyloric synapses to network stability, the mechanisms by which synapses promote phase maintenance were investigated. An artificial synapse that mimicked the feedforward PD to LP synapse, was used so that the synaptic parameters could be varied in a controlled manner in order to examine the influence of the properties of this synapse on the postsynaptic LP neuron. It was found that a static synapse with fixed parameters (such as strength and peak phase) across frequencies cannot result in a constant activity phase in the LP neuron. However, if the synaptic strength decreases and the peak phase is delayed as a function of frequency, the LP neuron can maintain a constant activity phase across a large range of frequencies. These dynamic changes in the strength and peak phase of the PD to LP synapse are consistent with the short-term plasticity properties previously reported for this synapse. In the pyloric network, the follower neuron LP provides the sole transmitter-mediated feedback to the pacemaker neurons. To understand the role of this synapse in network stability, this synapse was blocked and replaced by an artificial synapse using the dynamic clamp technique. Different parameters of the artificial synapse, including strength, peak phase, duration and onset phase were found to affect the pyloric cycle period. The most effective parameters that influence cycle period were the synaptic duration and its onset phase. Overall this study demonstrated that both the intrinsic properties of individual neurons and the dynamic properties of the synapses are essential in producing stable activity phases in this oscillatory network. The insight obtained from this thesis can provide a general understanding of the contribution of intrinsic properties to neuronal activity phase and how short-term synaptic dynamics can act to promote phase maintenance in oscillatory networks

Regulation of rhythmic activity in the stomatogastric ganglion of decapod crustaceans

Neuronal networks produce reliable functional output throughout the lifespan of an animal despite ceaseless molecular turnover and a constantly changing environment. The cellular and molecular mechanisms underlying the ability of these networks to maintain functional stability remain poorly understood. Central pattern generating circuits produce a stable, predictable rhythm, making them ideal candidates for studying mechanisms of activity maintenance. By identifying and characterizing the regulators of activity in small neuronal circuits, we not only obtain a clearer understanding of how neural activity is generated, but also arm ourselves with knowledge that may eventually be used to improve medical care for patients whose normal nervous system activity has been disrupted through trauma or disease. We utilize the pattern-generating pyloric circuit in the crustacean stomatogastric nervous system to investigate the general scientific question: How are specific aspects of rhythmic activity regulated in a small neuronal network? The first aim of this thesis poses this question in the context of a single neuron. We used a single-compartment model neuron database to investigate whether co-regulation of ionic conductances supports the maintenance of spike phase in rhythmically bursting “pacemaker” neurons. The second aim of the project extends the question to a network context. Through a combination of computational and electrophysiology studies, we investigated how the intrinsic membrane conductances of the pacemaker neuron influence its response to synaptic input within the framework of the Phase Resetting Curve (PRC). The third aim of the project further extends the question to a systems-level context. We examined how ambient temperatures affect the stability of the pyloric rhythm in the intact, behaving animal. The results of this work have furthered our understanding of the principles underlying the long-term stability of neuronal network function.Ph.D