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date: 24 June 2017

Stomatogastric Nervous System

Summary and Keywords

The crustacean stomatogastric nervous system contains a set of distinct but interacting rhythmic motor circuits that control movements of the foregut. When isolated, these circuits produce activity patterns that are almost perfect replicas of their behavior in vivo. The ease with which distinct circuit neurons are identified, recorded, and manipulated has provided considerable insight into the general principles of how motor circuits operate and are controlled at the cellular level. The small number of relatively large neurons has facilitated several technical advances in neuroscience research and allowed the identification of one of the earliest circuit connectomes. This enabled, for the first time, studies of circuit dynamics using the relationships between all component neurons of a nervous center. A major discovery was that circuits are not dedicated to producing a single neuronal activity pattern, and that the involved neurons are not committed to particular circuits. This flexibility results predominantly from the ability of neuromodulators to change the cellular and synaptic properties of circuit neurons. The relatively unique access to, and detailed documentation of, identified circuit, sensory, and descending pathways has also started new avenues into examining how individual modulatory neurons and transmitters affect their target cells. Groundbreaking experimental and modeling work has further demonstrated that the intrinsic properties of neurons depend on their recent history of activation and that neurons and circuits counterbalance destabilizing influences by compensatory homeostatic regulation of ionic conductances. The stomatogastric microcircuits continue to provide key insight into neural circuit operation in numerically larger and less accessible systems.

Keywords: central pattern generation, neuromodulation, sensorimotor, motor circuit, descending modulation, rhythmicity, oscillator, co-modulation, dynamic clamp, arthropod

Introduction

Much of our understanding about how neural circuits enable animals and humans to generate coordinated movements comes from studies of small neural circuits in invertebrate preparations. In particular, the study of central pattern generators (CPGs) that produce motor acts which, at their core, involve a rhythmically repeating set of movements, have contributed significantly to our present understanding about the motor circuits’ principles of function and oscillatory activity in the CNS in general (Barlow & Estep, 2006; Bässler, 1986; Briggman & Kristan, 2008; Burke, 2001; Cohen & Boothe, 2003; Dickinson, 2006; Grillner, 2006; Grillner, Markram, De Schutter, Silberberg, & LeBeau, 2005; Guertin, 2009; Harris-Warrick, 2011; Hooper, 2000; Katz & Hooper, 2007; Marder, 2000; Marder & Bucher, 2001; Marder & Calabrese, 1996; Selverston, 2005, 2010; Stein, 2015). The stomatogastric nervous system (STNS) of decapod crustaceans stands out as one of the premier systems for analyzing the properties, connections, and dynamics of individually identified neurons. In its almost 60 years as an experimental preparation, this system has provided new and exciting insights into the general operating principles governing oscillatory systems common to both vertebrates and invertebrates. With particular emphasis on circuit dynamics and neuromodulation, as well as descending and afferent control, publications from several decapod species have accumulated to about 1,000 journal articles, reviews, and books. Navigating this considerable amount of literature including studies using spiny and clawed lobsters, several species of crabs, crayfish, and shrimp can be a daunting task. This review highlights relevant findings and landmark papers whose influence and conclusions went beyond the STNS and small motor circuits and inspired whole new fields of study as they changed the understanding of neural circuit operation and control.

Circuit Analysis

Network Connectivity

The true value of the STNS results from its experimental accessibility, the use of several innovative techniques, and the combined research effort of many laboratories. This has allowed for the identification of general principles of neural function applicable to larger and less accessible systems. The STNS contains a set of motor circuits that control the movements of the crustacean foregut. It had long been known that at the center of the STNS, the stomatogastric ganglion (STG) houses approximately 30 neurons (Figure 1A) (Orlov, 1927, 1929). However, it was not until the work of Wilson (1961) showed that neuronal activity in an isolated nervous system can be a good representation of that in the intact animal, scientists realized isolated nervous systems provided an opportunity to study the mechanisms underlying behavior. In fact, CPGs can maintain their rhythmic activity when isolated from the rest of the nervous system, and in the absence of all rhythmic neuronal input, including proprioceptive sensory feedback. This idea was originally proposed by Thomas Graham Brown at the beginning of the 20th century (Brown, 1911) for the control of stepping movements in the vertebrate spinal cord. Graham Brown proposed mutually inhibitory connections between intrinsically active antagonistic “half-centers”—sets of neurons activated in alternation and capable of producing the basic pattern for stepping. Graham Brown also proposed proprioceptive sensory feedback modulating these “half-centers.” His conclusions were partly based on, but in contrast to, the findings of his mentor Sir Charles Sherrington, who had previously argued that rhythmic stepping movements were of peripheral origin and mediated via the flexion and crossed-extension reflex pathways (for reviews, see Guertin, 2012; Stuart & Hultborn, 2008).

Stomatogastric Nervous SystemClick to view larger

Figure 1. (A.) Provisional STG circuit diagram of the crab, Cancer borealis. Neurons are schematized along with their connections. Only synapses within the STG are shown. There are no excitatory synapses between STG neurons. Neurons are grouped according to which motor pattern they contribute to (green box: pyloric rhythm; blue box: gastric mill rhythm). The AB, PD, and LPG neurons build a pacemaker ensemble that drives the pyloric rhythm. Some neurons can contribute to more than one motor pattern and are highlighted in different colors. In the gastric mill network, the burst activities of Int1 and MG are shaped by synaptic inhibition from the pyloric pacemaker neurons. In the pyloric network, LPG, IC, and VD can at times be influenced by gastric mill activity. AM burst activity shows gastric mill timing, but AM innervates cardiac sac muscles. Modified from Stein (2009). (B.) Left: Darkfield view of the STG of Cancer borealis. The STG somata are arranged around the central neuropil (“n”). Their size and visibility allow identification of individual neuron types and reliable intracellular recordings from multiple neurons simultaneously. Right: Fluorescence staining of individual STG motor neuron in Cancer borealis. A GM neuron was injected with a green fluorescent dye (Alexa 488, Invitrogen) to visualize neuropilar structures and axons (“*”) that leave the STG and innervate striated muscles of the stomach. Figure courtesy of Carola Städele. (C.) Schematic representation of the STNS with its four ganglia and connecting nerves. The paired commissural ganglia (CoGs), the unpaired esophageal ganglion (OG), and the brain (supraesphageal ganglion) contain descending projection neurons that innervate the STG via the unilateral stomatogastric nerve (stn). The brain also connects to the CoG via the circumesophageal commissures (asterisks, connection not shown). STG motor neurons project mostly via posterior nerves toward their target muscles (not shown) in the gastric mill and pylorus. STG motor neurons are classified and named by their innervation targets. The motor nerves only contain a small number of axons. This allows the identification of individual STG neurons with extracellular recordings (for examples, see Fig. 2). The colored boxes illustrate the different stomach regions that are encompassed by the STNS (esophagus, cardiac sac, gastric mill, and pylorus). Motor nerves: mvn, median ventricular nerve; dgn, dorsal gastric nerve; lvn, lateral ventricular nerve; lgn, lateral gastric nerve; pdn, pyloric dilator nerve; pyn, pyloric constrictor nerve.

With the development of intracellular recording techniques in the 1960s came the first cellular evidence of the existence of CPGs, supporting Graham Brown’s original hypothesis. This caused a revolution in how some aspects of the nervous system were thought to work, and raised considerable hope to study an isolated but otherwise “normally” functioning neural center. Making extensive use of intracellular membrane potential recordings and the newly developed intracellular technique of injecting dyes into individual neurons, Don Maynard was the first to investigate the STG (Hartline & Maynard, 1975; Maynard, 1972). Owing to the small number of large cells, which can be electrophysiologically and morphologically identified in each and every animal, it became reasonable to think circuit functions could be understood in terms of the relationships between all component neurons—an impossibility just a few years earlier. Synaptic relationships between the neurons could be identified using paired intracellular recordings, making the STG one of the few neural circuits for which the functional connectome (the connectivity diagram, Figure 1B) had been identified early on (Eisen & Marder, 1982; Hartline & Gassie, 1979; Maynard, 1972; Maynard & Selverston, 1975; Miller & Selverston, 1982a; Mulloney & Selverston, 1974a, 1974b; Selverston & Miller, 1980; Selverston & Moulins, 1987; Selverston & Mulloney, 1974. For more recent reviews, see Daur, Nadim, & Bucher, 2016; Selverston, Szucs, Huerta, Pinto, & Reyes, 2009; Stein, 2009). This enabled a comprehensive mechanistic view of how the neural circuits work and led to the identification of many new basic concepts concerning cellular, synaptic, and circuit physiology, especially those concerned with the operation of CPGs. It quickly became clear that the wiring diagram of a circuit is necessary to understand how it works, but by itself it is not sufficient (Selverston & Moulins, 1987). As is true for most neural circuits, cellular and synaptic properties play a major role in determining circuit activity and dynamics (Bargmann & Marder, 2013). In the STG, these properties have been studied extensively due to the ability to record from the same neuron in many different experimental conditions.

Motor Patterns

Stomatogastric Nervous SystemClick to view larger

Figure 2. (A.) Intracellular recording of the pyloric dilator (PD) neuron, along with extracellular recordings of three motor nerves (pyn, pdn, and lvn), showing the main three phases of the pyloric rhythm. Individual STG neurons can be identified by their activity pattern, their timing in the rhythm, and by their amplitude and appearance on particular motor nerves. For example, only the two PD neurons (blue) are present on the pdn. The intracellular recording shows the rhythmic bursting of PD, as well as synaptic inhibition (“*”) from the lateral pyloric (LP) neuron. PY, pyloric constrictor neuron. (B.) Intracellular recordings of the lateral gastric neuron (LG) and its functional antagonist, the dorsal gastric (DG) neuron during a gastric mill rhythm. The gastric mill rhythm was initiated by stimulation of descending projection neurons from the brain (Hedrich & Stein, 2008). Note that this rhythm is about 10 times slower than the pyloric rhythm shown in (A). LG and DG can also be detected on the extracellular recordings shown below (lgn, dgn). GMs, gastric mill neurons.

The STNS generates motor patterns that control the movement of internal organs related to feeding. The most exhaustive characterization of circuit connectivities, dynamics, and modulation stem from studies in decapod crustaceans. The crustacean STNS consists of four interconnected ganglia: the paired commissural ganglia, the unpaired esophageal ganglion, and the STG (Figure 1C). The CPGs in the STNS generate the motor patterns underlying rhythmic movements of the esophagus (swallowing), cardiac sac (food storage), gastric mill (chewing), and pylorus (filtering of chewed food). The gastric mill and pyloric rhythms are generated by CPGs in the STG and have been studied in particular detail (Harris-Warrick, Marder, Selverston, & Moulins, 1992; Hartline & Maynard, 1975; Hooper, 2003; Hooper & DiCaprio, 2004; Marder & Bucher, 2007; Nusbaum & Beenhakker, 2002). The pyloric rhythm has a cycle period between 0.5 and 2 seconds (Figure 2A). It is driven by a set of electrically coupled pacemaker neurons and usually spontaneously active. The gastric mill rhythm is episodic and depends on the release of neuromodulatory substances from descending modulatory projection neurons. Pairs of neurons with reciprocal inhibitory connections create a rhythm with cycle periods between 5 and 20 seconds (Figure 2B). Most STG neurons have dual functions: They participate in pattern generation and act as motor neurons. They innervate striated muscles in the foregut of the animal, driving movements of the pyloric filter apparatus, and of teeth in the gastric mill (Claiborne & Selverston, 1987; Maynard & Dando, 1974; Selverston & Moulins, 1987).

Endogenous Bursting vs. Network Oscillations

The pyloric and gastric mill circuits differ in several aspects (Hartline & Maynard, 1975; Heinzel, Weimann, & Marder, 1993; Yarger & Stein, 2015). The gastric mill rhythm is about 10 times slower than the pyloric rhythm. It has two main phases (although this view has recently been challenged; see Selverston, Szucs, Huerta, Pinto, & Reyes, 2009), while the pyloric rhythm has three. Many neurons in the pyloric circuit were shown to have endogenous oscillatory (“pacemaker”) properties. For instance, the single anterior burster (AB) neuron is the main pacemaker (Ayali & Harris-Warrick, 1999; Hooper & Marder, 1987; Selverston & Moulins, 1987) and generates rhythmic bursts of activity even in the absence of synaptic input. Photoinactivation of AB completely eliminates the pyloric rhythm, whereas the same procedure in other STG neurons has very little influence on the rhythm (Miller & Selverston, 1982a). AB rhythmically inhibits all pyloric neurons who then rebound from inhibition (Perkel & Mulloney, 1974) when the AB burst ends. Although there is feedback from the follower neurons to the pacemaker, this feedback is not necessary for rhythm generation (Miller & Selverston, 1982a). In contrast, the gastric mill rhythm is mainly driven by the reciprocal inhibition between neurons. While the details of connectivity differ between species, it is clear that elimination of multiple neurons can eliminate the rhythm so no singular pacemaker neuron exists in this circuit (Selverston & Moulins, 1987). However, further detailed analyses of the STG connectivity and cellular neuron properties revealed it is an oversimplification to only attribute one rhythm-generating mechanism to each circuit. In both STG circuits, network and intrinsic mechanisms operate to some extent but with different weighting. The produced patterns result from cellular, synaptic, and network properties acting cooperatively (Selverston & Moulins, 1987). For example, the synaptic feedback from a follower neuron, the lateral pyloric neuron (LP), to the pyloric pacemaker, while not directly involved in driving the rhythm, can affect cycle period when active at certain phases of the rhythm. LP provides a negative feedback to changes in cycle period, which effectively stabilizes the speed of the pyloric rhythm (Thirumalai, Prinz, Johnson, & Marder, 2006). Similarly, in the gastric mill network, the intrinsic properties of the several network neurons, including the half-center neuron Int1 (Interneuron 1), appear to contribute to producing bursts (Mulloney & Selverston, 1974b; Russell & Hartline, 1984; Selverston, Szucs, Huerta, Pinto, & Reyes, 2009).

Intrinsic Properties Determine Cellular and Circuit Responses

One of the intrinsic properties that has a significant effect on the discharge of neurons and the activity of circuits was discovered at almost the same time in the stomatogastric system and in the crustacean cardiac ganglion: plateau potentials (Dickinson & Nagy, 1983; Russell & Hartline, 1978, 1982, 1984; Tazaki & Cooke, 1979a, 1979b). Brief depolarizing stimuli such as current injections, excitatory synaptic inputs, or release from inhibition trigger a sudden large amplitude depolarization that outlasts the stimulus and can be terminated by a hyperpolarizing pulse. During this “plateau” phase, the neuron fires, producing a long burst of action potentials. Plateau neurons can thus act as intrinsic “memories” of their last synaptic input (Marder, 1991) and cause history-dependent effects on the neuron’s activity. After their initial characterization, similar plateau properties were quickly discovered also in several types of mammalian neurons (Deschenes, Paradis, Roy, & Steriade, 1984; Kiehn & Eken, 1997, 1998; Kiehn, Johnson, & Raastad, 1996; Llinas & Sugimori, 1980; Schwindt & Crill, 1980).

Studies in the STG made extensive use of the voltage clamp technique. The ability to identify up to eight distinct ionic conductances in a single STG neuron (Buchholtz, Golowasch, Epstein, & Marder, 1992; Golowasch, Buchholtz, Epstein, & Marder, 1992; Turrigiano, LeMasson, & Marder, 1995) facilitated a host of studies that implicated individual currents in circuit output. For example, the A-current, a transient potassium current, is of particular interest for its role in shaping the pyloric rhythm phase relationships: Different STG neurons have distinct densities of A-current. The pyloric constrictor (PY) neurons have among the highest magnitude (Harris-Warrick, Coniglio, Barazangi, Guckenheimer, & Gueron, 1995; Tierney & Harris-Warrick, 1992), while in contrast, the LP neuron has a lower density of this current. This is interesting because one of the most resilient features of the pyloric rhythm is its phase constancy with LP always preceding PY across cycle frequencies, species, and neuronal sizes (Bucher, Prinz, & Marder, 2005; Hooper, 1997a; Soofi, Goeritz, Kispersky, Prinz, Marder, & Stein, 2014; Tang, Goeritz, Caplan, Taylor, Fisek, & Marder, 2010). When comparing the connectivities of PY and LP neurons in the pyloric network there is no clear reason why LP should generate its burst prior to PY as both neurons are inhibited by the pacemakers (Miller & Selverston, 1982b; Selverston & Miller, 1980). It appears the stronger A-current in PY contributes to producing a relatively slower rebound by transiently activating its potassium conductance during the rebound and thereby delaying PY activation when compared to LP (Harris-Warrick, Coniglio, Barazangi, Guckenheimer, & Gueron, 1995). Newer results confirm that synaptic properties indeed do not contribute to phase differences between LP and PY (Rabbah & Nadim, 2005). Thus, the A current demonstrates the influential effects a single current can have on the general shape and vitality of the rhythm. However, other intrinsic properties with slow temporal dynamics, such as changes in intracellular calcium concentrations, may also affect the regulation of phase relationships when the rhythm is perturbed (Hooper, 1997a, 1997b, 1998; Hooper, Buchman, Weaver, Thuma, & Hobbs, 2009).

Graded Transmitter Release

While already documented for nonspiking neurons (Graubard, 1978; Heitler & Pearson, 1980; Mendelson, 1971; Paul, 1976; Pearson & Fourtner, 1975), graded synaptic release from spiking neurons was first demonstrated as a proof of principle in lobster STG neurons. Using the inhibitory synapse between the presynaptic LP neuron and the postsynaptic pyloric dilator (PD) neuron, work from the Hartline laboratory (Graubard, Raper, & Hartline, 1980, 1983) showed that transmitter release can occur at subthreshold membrane potentials in neurons capable of producing action potentials. Even when LP is oscillating below the threshold for spike initiation, it can still release neurotransmitters and effectively inhibit the postsynaptic PD, shaping the final circuit output. During suprathreshold LP activity, both spike-evoked and graded release contribute to the postsynaptic response. The graded transmission comprises the major component during an ongoing rhythm (Hartline, Russell, Raper, & Graubard, 1988; Raper, 1979), while the spike-mediated component is comparably small. Subsequent studies have then demonstrated subthreshold influences on synaptic transmission in other invertebrate and vertebrate preparations (Debanne, Bialowas, & Rama, 2013; Ivanov & Calabrese, 2006a, 2006b). It was later shown that spike-mediated components can be based on high threshold calcium currents that are distinct from the low-threshold calcium currents that elicit graded responses (Angstadt & Calabrese, 1991; Ayali, Johnson, & Harris-Warrick, 1998; Ivanov & Calabrese, 2000; Lu, Dalton, Stokes, & Calabrese, 1997; Olsen & Calabrese, 1996). Therefore, to fully understand a network, one must know the relationship between presynaptic membrane potential, transmitter release threshold, and release dynamics.

Entrainment by Descending Fibers

The definition of central pattern generators states that CPGs can operate independently of any external timing input (Bucher, Haspel, Golowasch, & Nadim, 2015; Delcomyn, 1980). However, in addition to the core CPGs, there are rhythmic inputs such as sensory and descending neurons that can entrain the circuit. Like all motor systems, the STNS receives proprioceptive sensory feedback that is rhythmic in nature as it is in response to the rhythmic activity of the STG motor neurons (Birmingham, Szuts, Abbott, & Marder, 1999; Katz & Harris-Warrick, 1989, 1991; Simmers & Moulins, 1988a; Smarandache, Daur, Hedrich, & Stein, 2008). In addition, however, the STG is innervated by projection neurons that are involved in the selection and activation of the gastric mill motor pattern (Coleman, Nusbaum, Cournil, & Claiborne, 1992; Hartline & Russell, 1984; Norris, Coleman, & Nusbaum, 1994, 1996; Nusbaum & Marder, 1989; Selverston & Moulins, 1987). These projections are also involved in the modulation and maintenance of the pyloric rhythm (Dando & Selverston, 1972; Dickinson & Nagy, 1983; Marder, Hooper, & Siwicki, 1986; Nagy, Dickinson, & Moulins, 1981; Nusbaum & Marder, 1989; Russell & Hartline, 1978, 1981; Sigvardt & Mulloney, 1982). They descend from the brain, the commissural ganglia, or the esophageal ganglion (Figure 1C). Although the modulatory actions of projection neurons are necessary for activating the STG circuits, rhythmic input from these descending fibers is not a prerequisite for generating rhythmic STG activity. Nevertheless, projection neuron activity can already be patterned, which, as a consequence, entrains the STG motor rhythms or affects their phase relationships (Blitz & Nusbaum, 2012; Combes, Meyrand, & Simmers, 1999; Hedrich, Smarandache, & Stein, 2009; Hedrich & Stein, 2008; Moulins & Nagy, 1982; Simmers & Moulins, 1988b).

The best characterized example for how rhythmic descending input affects the STG motor circuits is the modulatory commissural neuron 1 (MCN1) in the crab, Cancer borealis. MCN1 is activated by a variety of sensory pathways. Its activity is rhythmic in vitro (Blitz & Nusbaum, 2008) and in vivo (Hedrich, Diehl, & Stein, 2011). MCN1’s rhythmic activity entrains the gastric mill circuit via its slow metabotropic actions on the gastric mill half-center neuron LG and its fast ionotropic influences on LG’s antagonist Int1 (DeLong, Kirby, Blitz, & Nusbaum, 2009; Stein, DeLong, Wood, & Nusbaum, 2007). Additionally, transmitter release from MCN1 is rhythmic, even when MCN1’s spike activity is forced to be tonic: Local presynaptic inhibition of the MCN1 terminals by LG leads to a sawtooth-like wave of transmitter release from MCN1 that influences gastric mill period and LG bust durations (Coleman, Meyrand, & Nusbaum, 1995; Coleman & Nusbaum, 1994). Finally, MCN1 receives ascending feedback from the pyloric pacemaker AB, which imposes pyloric timing upon the MCN1 burst. These timing influences are conditional (Blitz & Nusbaum, 2008), but if present determine the burst pattern of the LG motor neuron, causing functional changes in the behavioral output (Diehl, White, Stein, & Nusbaum, 2013; Spencer & Blitz, 2016). The behavior in an intact animal thus appears to be the result of a cascade of coupled oscillators, with extrinsic timing input to CPGs controlling frequency, type of rhythm produced, and intercircuit connections, while the CPG itself is mainly concerned with generating the basic rhythmic pattern that enables the movement.

Techniques and Innovations

Photoinactivation of Neurons

Studies in the STG have been accompanied by the development and use of several innovative techniques facilitating circuit analysis and modulation. Besides the early and rigorous use of the previously developed current- and voltage-clamp recordings, several techniques originally developed with the STG system were key to obtaining a cellular-level understanding of STG circuit operation. Photoinactivation selectively eliminates neurons from a circuit to test the influence of individual circuit components on whole circuit performance (Miller & Selverston, 1979). Photoinactivation is based on intracellular staining techniques developed to determine the cellular structure of neurons in live preparations using fluorescent dyes (Stewart, 1978, 1981; Stretton & Kravitz, 1968). Using the phototoxicity of these dyes when illuminated with high-intensity light, it became possible to selectively disable stained cells without harming non-stained cells.

Dynamic Clamp

Second, the dynamic clamp was developed to selectively introduce specific ionic or synaptic currents (Sharp, O’Neil, Abbott, & Marder, 1993a, 1993b). This technique essentially builds a brain-machine interface that connects a computer model of a neuron or a current in real time with the biological neuron. To do that, the dynamic clamp uses real-time recorded membrane potentials to calculate a model current. It then injects the resulting current either into the recorded neuron (in the case of an intrinsic current) or into a designated postsynaptic neuron (in the case of synaptic currents). The dynamic clamp benefited significantly from the detailed knowledge of ionic currents in the STG that were characterized with voltage-clamp recordings (Golowasch & Marder, 1992) and sophisticated mathematical models that existed for these currents (Buchholtz, Golowasch, Epstein, & Marder, 1992; Golowasch, Buchholtz, Epstein, & Marder, 1992; Marder et al., 1993). For example, insight into intercircuit interactions between the pyloric and gastric mill rhythms derived from computational models (Nadim, Manor, Nusbaum, & Marder, 1998) were verified by injecting synaptic currents via dynamic clamp in the biological circuits (Bartos, Manor, Nadim, Marder, & Nusbaum, 1999). The use of both photoinactivation and dynamic clamp has now extended well beyond the stomatogastric system.

Computer Modeling

Computer modeling of circuit activity has greatly facilitated our understanding of circuit function. Starting in the early days of STG research (Mulloney, Perkel, & Budelli, 1981; Perkel & Mulloney, 1974; Russell & Hartline, 1981), it has become a valuable tool for elucidating neural circuit operation in general. While initially hand-tuned models of single neurons or reduced networks helped decipher the underlying circuitry and basic neuronal properties (Buchholtz, Golowasch, Epstein, & Marder, 1992; Selverston & Moulins, 1987), today models of fully functional STG networks exist and millions of parameter combinations and their effects on circuit output and circuit dynamics can be tested (Abbott & LeMasson, 1993; Bose, Golowasch, Guan, & Nadim, 2014; Drion, O’Leary, & Marder, 2015; Falcke, Huerta, Rabinovich, Abarbanel, Elson, & Selverston, 2000; Gutierrez, O’Leary, & Marder, 2013; Kintos, Nusbaum, & Nadim, 2016; LeMasson, Marder, & Abbott, 1993; O’Leary, Williams, Franci, & Marder, 2014; Prinz, Billimoria, & Marder, 2003a; Prinz, Bucher, & Marder, 2004; Selverston et al., 2000; Stein & Ausborn, 2004; Taylor, Goaillard, & Marder, 2009; Willms, Baro, Harris-Warrick, & Guckenheimer, 1999). Complimentary experimental evidence of model predictions demonstrates that modeling is instrumental in determining the function of several cellular and synaptic properties of STG circuit neurons (Goldman, Golowasch, Marder, & Abbott, 2001; Golowasch et al., 2009; Soofi, Archila, & Prinz, 2011).

Immunocytochemistry

Finally, immunocytochemistry was heavily used to identify neurotransmitters and modulators in the STNS (reviews: Skiebe, 2001, 2003). More recently, mass spectrometry (Billimoria, Li, & Marder, 2005; Cape, Rehm, Ma, Marder, & Li, 2008; Chen, Hui, Sturm, & Li, 2009a; Chen, Ma, Hui, & Zhang, 2009b; Li et al., 2003) and single-cell transcriptomics and mRNA measurements (Garcia, Daur, Temporal, Schulz, & Bucher, 2015; McGrath, Vollmer, Kaluziak, & Ayers, 2016; Schulz, Goaillard, & Marder, 2006, 2007; Temporal, Lett, & Schulz, 2014) have supplemented this approach.

Neuromodulation and Plasticity

The Composition of Neural Circuits Is Flexible

In the early days of circuit analysis, it was believed that the neuronal basis of a particular behavior could be understood once all of the neurons and synapses in a circuit were identified. Inherent to this belief was that the core circuits are dedicated to particular behaviors or to specific functions within a behavior. Accordingly, most studies initially assigned neurons to certain circuits based on their activity under defined behavioral conditions. Yet, most muscles are used in more than one behavior or distinct variants of a behavior. Work in the STNS has demonstrated that rather than employing separate circuits, the same pattern-generating elements can be used to generate various distinct behaviors. The degree of flexibility afforded to the STNS networks is extensive. The isolated CPGs in the STNS have the fortuitous property of including motor neurons as part of the pattern-generating networks (Maynard, 1972). Consequently, recordings from the CPG neurons provide a good representation of the behavioral output, as well as insights into the mechanisms underlying these behaviors. Initially, a total of four distinct motor patterns were identified in the STNS: The aforementioned pyloric and gastric mill rhythms, as well as the cardiac sac and esophageal motor patterns. Each STG neuron was assigned to one of the four networks underlying these rhythms. However, it was soon found that a given neuron can contribute to several motor patterns, for example, to the pyloric rhythm, to the gastric mill rhythm, or to both simultaneously (Clemens, Combes, Meyrand, & Simmers, 1998a). In the spiny lobster, the ventricular dilator (VD) neuron can drop out of the pyloric rhythm and become part of the much slower cardiac sac rhythm (Hooper & Moulins, 1989, 1990; Hooper, Moulins, & Nonnotte, 1990). When the cardiac sac rhythm is activated by sensory stimulation, VD loses the ability to generate plateau potentials, and the strong synaptic drive from modulatory neurons causes it to fire in time with the cardiac sac rhythm. When both rhythms are active simultaneously, VD can switch freely between the two.

Likewise, previously separate circuits can be combined into a new composite circuit. The peptide, red pigment concentrating hormone (RPCH), strongly increases the synaptic connection between neurons that provide input to both the cardiac sac and gastric mill circuits. This causes a new conjoint alternating pattern in which elements of both the cardiac sac and gastric mill circuits participate (Dickinson & Marder, 1989; Dickinson, Mecsas, & Marder, 1990).

On the flip side, STG circuits can be dismantled and reconfigured. The gastric mill, pyloric, and esophageal circuits generate distinct motor rhythms in control conditions. However, in lobsters, the activation of the modulatory pyloric suppressor (PS) neurons eliminates these individual rhythms and replaces them with a single, conjoint motor pattern (Meyrand, Simmers, & Moulins, 1991, 1994). Specifically, while exciting a subset of neurons in all three circuits, the PS neurons inhibit selected gastric mill and pyloric circuit neurons, which prevents these neurons from participating in the conjoint rhythm.

These studies provide concrete evidence that CPG neurons and circuits are multifunctional, and the neuronal composition of a CPG network varies continuously with the state of the system. More generally, this shows that motor patterns and their underlying circuits may be assembled and disassembled depending on the current needs and conditions of the animal and its behavior.

Neuromodulation Allows a Single Circuit to Produce Multiple Outputs

The discovery that CPGs are not dedicated to producing a single neuronal activity pattern but are rather functionally flexible marked a paradigm shift in our understanding of neural plasticity and in STG research. While knowing the connectome is a major necessary contribution to research on neuronal mechanisms, it is in no way sufficient for a complete understanding of circuit dynamics and behavioral output. This conclusion substantially complicates network analyses, especially in larger and less accessible networks, but highlights the shortcomings of traditional approaches to circuit analysis. STG flexibility results largely from the presence of different metabotropic neuromodulators and their ability to change the cellular and synaptic properties in STG neurons. Combining the efforts of all the labs working on the STG, it was shown that every neuron and synapse in the pyloric circuit is subject to neuromodulation (Harris-Warrick, Johnson, Peck, Kloppenburg, Ayali, & Skarbinski, 1998; Johnson, Peck, & Harris-Warrick, 1995). This is likely also true for the gastric mill network. An ever-growing number of modulators have been shown to affect STG neurons, with the modulator number far outmatching that of the neurons. One of the lessons from these studies is that the intrinsic and synaptic properties of the neurons involved in a circuit must be considered from a dynamic point of view. They are continuously modified by extrinsic modulatory influences that not only quantitatively change circuit output, but qualitatively rewire the circuit and lead to distinct behavioral outputs. This means contrary to the initial idea that CPGs produce only a stereotyped output, CPGs actually produce a repertoire of distinct outputs—a fact that has been verified in a number of different systems now, including CPG circuits in mammals (for reviews, see Bargmann, 2012; Briggman & Kristan, 2008; Büschges, Scholz, & El Manira, 2011; Dick et al., 2015; Doi & Ramirez, 2008; Nistri, Ostroumov, Sharifullina, & Taccola, 2006; Nusbaum & Blitz, 2012; Taghert & Nitabach, 2012).

Sources and Types of Modulators

In the STNS, neuromodulators can reach the STG motor circuits either via release from the terminals of modulatory projection neurons or sensory neurons, or as circulating hormones via the bloodstream (Dickinson, 2006; Nusbaum & Beenhakker, 2002; Stein, 2009; Thirumalai & Marder, 2002). Neurohormones are released by neurohemal organs and neurohemal release zones such as the x-organ/sinus gland complex in the eyestalks, the postcommissural organ, and the pericardial organs (Chen, Ma, Hui, Zhang, & Li, 2009b; Christie, Skiebe, & Marder, 1995; Li et al., 2003; Pulver & Marder, 2002; Skiebe, 2001, 2003). The STG is ideally located to receive hormonal modulation because it is situated within a major blood vessel, the ophthalmic artery. Neuromodulatory substances are also released from modulatory neurons innervating the STG. The complement of neuromodulatory substances in modulatory neurons varies during development (Cape, Rehm, Ma, Marder, & Li, 2008; Fenelon, Kilman, Meyrand, & Marder, 1999; Kilman, Fenelon, Richards, Thirumalai, Meyrand, & Marder, 1999) and between equivalent neurons in different species, as does the response of the motor circuits (Rehm, Deeg, & Marder, 2008a; Rehm, Taylor, Pulver, & Marder, 2008b).

While initially immunocytochemistry was used to identify modulatory substances, mass spectrometry has pushed the limits of identifying neuropeptides. It is now possible to identify peptides in individual neurons and to quantitatively determine modulator concentrations in specific tissues (Chen, Ouyang, Xiao, & Li, 2014; Garcia, Daur, Temporal, Schulz, & Bucher, 2015) and the hemolymph (Ye, Hui, Kellersberger, & Li, 2013). The sheer number of modulators (>100 at present (Billimoria, Li, & Marder, 2005; Marder & Bucher, 2007; Marder & Thirumalai, 2002; Skiebe, 2001)) present in the STG is staggering. Further, all major types of modulators are found, including classic neurotransmitters, gaseous transmitters, biogenic amines, and many neuropeptides. However, the count includes several isoforms of the same family, and it is not clear whether distinct responses are elicited or promiscuous receptors are activated (Cruz-Bermudez, Fu, Kutz-Naber, Christie, & Li, 2006; Dickinson et al., 2015).

In the isolated STG, bath applications of various neuromodulators reproducibly elicit stable pyloric and gastric mill rhythms (Marder & Thirumalai, 2002; Marder & Weimann, 1992). The pyloric rhythm is continuously active and can be reconfigured by neuromodulators into very distinct rhythmic patterns. The gastric mill rhythm is less often spontaneously active, but can be activated by modulatory projection neurons to generate distinct patterns. Several in vivo studies have shown that varies types of gastric mill rhythms exist in intact animals (Diehl, White, Stein, & Nusbaum, 2013; Heinzel, 1988) and both, pyloric and gastric mill rhythms are altered after feeding and changes in sensory conditions (Clemens, Massabuau, Legeay, Meyrand, & Simmers, 1998b; Fleischer, 1981; Yarger & Stein, 2015).

In contrast to ionotropic transmitters that directly open ion channels, the actions of metabotropic transmitters are slower and include phosphorylation sites of their target channel proteins. The canonical pathway involves activation of a second messenger pathway via G-protein coupled receptors and a subsequent activation of kinases and phosphatases that act on the channel. For example, imaging studies have shown that cyclic AMP is uniquely upregulated by different modulators (Hempel, Vincent, Adams, Tsien, & Selverston, 1996), and long-term modulation by some amines employs the D1R-protein kinase A axis (Krenz, Parker, Rodgers, & Baro, 2014). Neuromodulators primarily affect activation, inactivation, and current amplitude of voltage- and time-dependent ionic currents (Bucher & Marder, 2013; Kaczmarek & Levitan, 1987; Marder & Meyrand, 1989). Since these currents are defined by their voltage dependence, they are only active within a certain voltage range outside of which they have no influence. Modulation of a channel’s active voltage range can thus significantly change channel function, resulting in pronounced effects on the circuits in which impacted neurons act. Transmitters with ionotropic actions, in contrast, typically only affect the magnitude of the elicited current. Work on a large number of neuron types in the STG has pioneered modulator actions on neurons and circuits. It is now generally accepted that ion channel modulation is ubiquitous in all systems, and modulatory control of ion channels is the rule rather than the exception.

Convergence and Divergence of Modulator Actions

The ionic mechanisms underlying bursting STG neurons have been extensively studied (Buchholtz, Golowasch, Epstein, & Marder, 1992; Golowasch & Marder, 1992). At least eight different conductances contribute to their rhythmic bursting (Buchholtz et al., 1992; Golowasch et al., 1992). Together with the availability of so many different neuromodulators this has provided an opportunity to determine the extent to which modulator actions converge and diverge at the cellular level. The main pacemaker AB, for example, is a “conditional burster,” that is, it requires extrinsic (but not rhythmic!) modulatory input. It is strongly excited by a number of amine modulators, including dopamine, serotonin, and octopamine (Flamm & Harris-Warrick, 1986; Katz, 1998). However, AB’s response to these modulators is quite distinct, showing differences in burst period, slow wave membrane potential oscillation, and voltage range covered. This is because each amine uses a distinct ionic mechanism to elicit bursting in AB (Harris-Warrick & Flamm, 1987). These actions of amine modulators have also been studied on the circuit level, showing divergent cellular and synaptic actions. They are cell type as well as modulator specific and include distinct actions on multiple ion channels in each cell type (Harris-Warrick, 2011). Consequently, different amines elicit distinct versions of the pyloric rhythm (Harris-Warrick, Johnson, Peck, Kloppenburg, Ayali, & Skarbinski, 1998; Harris-Warrick, Marder, Selverston, & Moulins, 1992). Dopamine has been studied in particular detail, demonstrating that amines affect a diverse set of ionic currents by acting via several different receptors (Clark, Khan, & Baro, 2008). This enables dopamine to elicit diverse circuit responses and dynamics at multiple concentrations and over various time courses.

In contrast, neuropeptides and muscarinic agonists have mostly convergent actions in the STG. They all activate the same inward current (the modulator-induced current IMI; Gray & Golowasch, 2016; Swensen & Marder, 2000). IMI acts as a pacemaker current and supports oscillatory activity in STG neurons. While IMI’s voltage dependence is partly regulated by intra- and extracellular calcium, it is mostly the negative slope conductance of its IV curve that supports oscillatory activity (Bose, Golowasch, Guan, & Nadim, 2014; Zhao, Golowasch, & Nadim, 2010). Each pyloric neuron is directly targeted by several peptide modulators and when isolated from the network, responds similarly to all modulators. Accordingly, since all peptides affect the same ionic current, a given neuromodulator may occlude the actions of others when present at the same time. Despite this convergence at the current level, each peptide modulator elicits a distinct pyloric rhythm (Marder & Hooper, 1985).

This is partly due to a differential expression of receptors within the full network, such that different modulators will activate different combinations of neurons, resulting in distinct motor patterns from the same network. For example, bath application of the peptides proctolin and crustacean cardioactive peptide (CCAP) elicit different versions of the pyloric rhythm. Swensen and Marder (2001) elegantly confirmed that receptor distribution is a factor in determining which neurons participate in a motor pattern. Using dynamic clamp, they selectively added IMI into those pyloric neurons that respond to proctolin but not to CCAP while bath applying CCAP. This caused the pyloric rhythm to switch from the CCAP version to the proctolin-induced version. Finally, in addition to receptor distribution, the level of receptor expression can differ between cell types and affect circuit output (Garcia, Daur, Temporal, Schulz, & Bucher, 2015).

Many neuropeptides are released as cotransmitters from projection and sensory neurons. Consequently, differences in the temporal structure and quantitative release of modulators may also affect circuit output (Kintos, Nusbaum, & Nadim, 2016). Furthermore, several STG studies have shown that cotransmitters can be released differentially into different target areas or may differentially affect their target neurons independent of receptor presence (Blitz & Nusbaum, 1999; Kwiatkowski, Gabranski, Huber, Chapline, Christie, & Dickinson, 2013). For example, the gastric mill LG neuron is activated by one of two peptide transmitters of the descending projection neuron MCN1. Its small molecule cotransmitter, GABA, selectively activates Int1, but not LG, although LG possesses receptors for GABA (Stein, DeLong, Wood, & Nusbaum, 2007). Consequently, the spatial pattern of release may matter for the elicited motor pattern as well.

Modulation of Synapses and Consequences for Circuit Output and Behavior

While many studies address the actions of neuromodulators on cellular properties, there is evidence of neuromodulators directly affecting electrical and chemical synaptic transmission in the STG (Ayali, Johnson, & Harris-Warrick, 1998; Ayali & Harris-Warrick, 1999; Harris-Warrick, Johnson, Peck, Kloppenburg, Ayali, & Skarbinski, 1998; Marder, Jorge-Rivera, Kilman, & Weimann, 1997; Thirumalai, Prinz, Johnson, & Marder, 2006). In the lobster, Panulirus interruptus, all electrical synapses in the pyloric network are affected by amine modulators. Johnson, Peck, and Harris-Warrick (1993, 1994) showed that dopamine, serotonin, and octopamine have distinct effects on each synapse. For example, the electrical coupling between the AB pacemaker and PD is non-rectifying in control conditions. Dopamine enhances the electrical coupling strength selectively in the PD to AB direction. In the opposite direction, it weakens the synapse, essentially creating a rectifying electrical synapse. Serotonin, on the other hand, while enhancing the synapse in the PD to AB direction, has no effect on the opposite direction.

The effects of amines on chemical synapses have also been studied in exquisite detail (Dickinson et al., 1990; Harris-Warrick, Johnson, Peck, Kloppenburg, Ayali, & Skarbinski, 1998). Every chemical synapse in the pyloric network of the spiny lobster has been shown to be affected by dopamine. Much like for intrinsic cell properties, the effects of dopamine on synapses are manifold (summary: Harris-Warrick, 2011; Harris-Warrick et al., 1998; Harris-Warrick & Johnson, 2010). While some synapses are effectively inactivated by dopamine, others are enhanced and only functional in the presence of dopamine. Examples for the former are the synapses of the PD neurons, which essentially fall silent in dopamine. In contrast, the synaptic connection between the pyloric constrictor neurons and the LP neuron requires dopamine to be functional (Johnson, Peck, & Harris-Warrick, 1994). The effects of dopamine on this synapse are mediated pre- and postsynaptically (Johnson & Harris-Warrick, 1997; Johnson, Kloppenburg, & Harris-Warrick, 2003). Interestingly, dopamine had opposing effects in different release sites of the same presynaptic neuron. STG neurons make multiple contacts with their postsynaptic partners (King, 1976), but only a subset of presynaptic varicosities showed an increased calcium accumulation in dopamine, while other showed no change or the opposite (Kloppenburg, Zipfel, Webb, & Harris-Warrick, 2007). These data thus indicate that DA is regulating the strength of this synaptic connection by having opposing effects in different presynaptic terminals (Harris-Warrick & Johnson, 2010). Dopamine even has differential effects on spike-mediated and graded transmission. While it diminishes spike-mediated transmission of the LP to PD synapse, it strengthens the graded transmission at the same synapse (Ayali, Johnson, & Harris-Warrick, 1998). The molecular mechanisms underlying this differential modulation are unknown, but it has been shown that both presynaptic transmitter release and postsynaptic response can be modulated by dopamine (Harris-Warrick et al., 1998).

Long-term changes in synaptic strength such as LTP are usually assumed to have direct consequences for circuit output and behavior. However, these consequences may not necessarily be obvious. The LP neuron provides the sole feedback synapse from the pyloric network to the pacemakers. As this inhibitory synapse represents a negative feedback loop, it was assumed to decrease the cycle period of the pattern generator (Ausborn, Wolf, & Stein, 2009). However, although this synapse is potentiated by the peptide RPCH, it does not alter the pyloric cycle period (Thirumalai, Prinz, Johnson, & Marder, 2006). Analyses of phase response curves depict that the timing and duration of the synaptic input from LP failed to phase-advance or phase-delay the next burst of the pacemakers (Prinz, Thirumalai, & Marder, 2003b). Instead, it appears that the strengthened synapse acts to stabilize the rhythm (Mamiya & Nadim, 2004; Nadim, Zhao, Zhou, & Bose, 2011; Zhao, Sheibanie, Oh, Rabbah, & Nadim, 2011) and reduces variations in cycle period. Here, the neuromodulatory control of synaptic strength may therefore tend to stabilize ongoing network performance. An important conclusion from these studies is that neuromodulation does not always have obvious consequences at the network level, but might rather change the response of the circuit to perturbation. This emphasizes the need to examine the whole circuit, rather than only individual synapses, and reminds us that a circuit diagram is insufficient to understand circuit dynamics—it is more like a roadmap: It shows the streets, but not the traffic.

Besides changing synaptic strength, neuromodulators act on synaptic dynamics, such as facilitation and depression, and can influence long-term synaptic modifications in the context of learning and memory (Giurfa, 2006). In the STG, the effects of modulators on synaptic dynamics can be drastic. The peptide proctolin, for example, not only enhances the LP to PD synapse in the pyloric network, but additionally switches the synapse from being depressed during pyloric oscillations to being facilitated (Zhao et al., 2011). In an oscillatory system, modulation of short-term synaptic dynamics can thus effectively act as a gain-control mechanism modifying synaptic transmission as a function of the oscillator cycle period.

Modulation of Circuit Input Pathways

Not only does neuromodulation affect circuit properties, it also affects circuit inputs and outputs. A number of STG studies have identified sensory neurons as sources and targets of modulators. The gastropyloric receptors (GPRs) are muscle stretch-receptors that arborize in the STG and the commissural ganglia (Birmingham, 2001; Katz, 1998; Katz, Eigg, & Harris-Warrick, 1989; Katz & Harris-Warrick, 1989). In C. borealis, the GPRs contain acetylcholine, allatostatin, and serotonin (Katz, Eigg, & Harris-Warrick, 1989; Katz & Harris-Warrick, 1989; Skiebe & Schneider, 1994). Their modulatory actions are manifold, including long-lasting excitation of the pyloric pacemaker and changes in the pyloric cycle frequency and phase relationships via distinct serotonin receptors (Katz, 1998; Katz & Harris-Warrick, 1989; Zhang & Harris-Warrick, 1994). Furthermore, serotonin alters the intrinsic properties of the gastric mill neuron DG, enabling it to generate plateau potentials triggered by GPR’s cholinergic depolarization (Katz & Harris-Warrick, 1989; Kiehn & Harris-Warrick, 1992a, 1992b; Zhang & Harris-Warrick, 1995; Zhang, Wootton, & Harris-Warrick, 1995). Finally, the GPRs modulate descending projection neurons in the commissural ganglia which, in turn, modulate STG motor patterns (Blitz, Beenhakker, & Nusbaum, 2004). Consequently, sensory receptors in rhythmic systems elicit long-lasting responses from motor circuits via their modulatory action in addition to cycle-by-cycle feedback.

Sensory neurons not only have modulatory actions, but are also targeted by neuromodulators. The temporal precision of sensory spiking in GPR, for example, is altered by allatostatin (Billimoria, DiCaprio, Birmingham, Abbott, & Marder, 2006; Birmingham, Billimoria, DeKlotz, Stewart, & Marder, 2003). More drastic effects are seen in the anterior gastric receptor (AGR), a muscle tendon organ (Combes, Simmers, & Moulins, 1997; Smarandache & Stein, 2007), whose activity mode can be switched from encoding cycle-by-cycle muscle movements to detecting average levels of muscle tension by a peptide modulator (Birmingham, Szuts, Abbott, & Marder, 1999). AGR possesses an additional ectopic spike initiation zone in its axon (Daur, Nadim, & Stein, 2009). This spike initiation zone generates a low-frequency spontaneous activity by neuromodulation: AGR’s ectopic spike activity is influenced by local motor neurons, but this sensorimotor interaction is state dependent. Focal axon modulation by octopamine can abolish axonal signal integration by dislocating the site of spike initiation to a distant region along the axon (Städele & Stein, 2016). Modulation thus enables sensory neurons to encode stimuli differently, alters the relationship between sensory stimulus and the resulting spike activity, and alters spike frequency on its way to target neurons. This has functional consequences and can, for example, change the state of the motor system such that it responds differently to other inputs (Daur, Nadim, & Stein, 2009).

Modulation of Circuit Output Pathways

Even after the motor activity is generated, neuromodulators can alter the behavioral output. For one, the temporal dynamics of action-potential propagation can be affected. Dopamine, for example, affects the history dependence of action-potential propagation velocity in PD motor neurons, such that spike frequencies have changed significantly by the time they arrive at the muscle (Ballo, Nadim, & Bucher, 2012). Dopamine and other amines can also excite the axons of other STG motor neurons and create additional action potentials with complex effects on spike frequency (Bucher, Thirumalai, & Marder, 2003; Meyrand, Weimann, & Marder, 1992).

Once the action potentials arrive at the target muscles, the muscle contractions are subject to neuromodulation (Jorge-Rivera, Sen, Birmingham, Abbott, & Marder, 1998; Jorge-Rivera & Marder, 1996, 1997; Meyrand & Marder, 1991). An extensive study by Jorge-Rivera and colleagues (1998) demonstrated that the gastric mill gm4 muscle activity is modulated by many different neurohormones in the hemolymph (Christie, Skiebe, & Marder, 1995; Skiebe, 2001). Both the amplitude of the muscle force and its temporal dynamics are affected. For example, TNRNFLRFamide and serotonin increase force amplitude, but their effects are distinct when motor neuronal firing frequencies are taken into account: While TNRNFLRFamide more effectively potentiates gm4 contractions at 10 Hz firing than at 40 Hz, serotonin is more effective at 40 Hz (Jorge-Rivera, Sen, Birmingham, Abbott, & Marder, 1998). Thus, the effects of modulators depend in a complex way on the prevailing motor activity. They may act as a gain control mechanism that adjusts the response of the effectors to different motor circuit frequencies. Some of the modulatory effects on the muscles are mediated via a modulation of the neuromuscular junction, that is, the modulation of a synapse rather than intrinsic muscle properties (Atwood, 1976; Atwood & Wojtowicz, 1986; Dudel & Kuffler, 1961; Jorge-Rivera, Sen, Birmingham, Abbott, & Marder, 1998; Jorge-Rivera & Marder, 1996, 1997; Katz, Kirk, & Govind, 1993; Morris & Hooper, 2001; Msghina, Govind, & Atwood, 1998; Qian & Delaney, 1997; Sen, Jorge-Rivera, Marder, & Abbott, 1996; Stein, Smarandache, Nickmann, & Hedrich, 2006; Worden, Bykhovskaia, & Hackett, 1997). For example, in the case of the gm1 muscle, serotonin increases the synaptic strength by almost 50%, most likely due to presynaptic effects (Jorge-Rivera et al., 1998).

Neuromodulator effects on muscles and motor circuits may even be coordinated. For example, besides affecting the pyloric LP neuron, hormonally released crustacean cardioactive peptide influences LP-innervated muscles (Weimann et al., 1997). The changes in the motor pattern caused by the excitatory effects of this peptide on LP produce significant changes in LP-innervated muscle movement. The elicited muscle contractions are then further potentiated by the peptide’s effects on the neuromuscular junction. Thus, motor neuron firing and the gain control of the neuromuscular junctions can operate in conjunction in response to hormonally released neuromodulators.

Variability and Stability of Circuit Activity

Similar Activity Despite Disparate Circuit Properties

A critical factor determining network activity is the availability and strength of ionic conductances in network neurons. STG neurons have qualitatively similar complements of ionic currents, but each neuron has a distinct footprint because relative magnitudes of currents differ between neuron types (Garcia, Daur, Temporal, Schulz, & Bucher, 2015). However, the intrinsic properties of even individual cells do not seem to be tightly controlled. For example, mRNA and conductance levels of the slow hyperpolarization-activated current (Ih) varies up to 4-fold in PD neurons of different animals of the same species (Schulz, Goaillard, & Marder, 2006, 2007). Yet, the generated motor patterns and activity waveforms of the neurons are virtually identical. This is surprising since changes in Ih conductance can be sufficient to modify the motor pattern (Peck, Gaier, Stevens, Repicky, & Harris-Warrick, 2006). It raises the question how cell and circuit phenotypes, as well as cell identities are maintained despite the variability of the component currents. It also challenges the view that identified neurons possess similar properties in each individual, and mean values obtained from many experiments are a meaningful representation of the real state of the system.

Research using the STG has been on the forefront of exploring these issues. Modeling and experimental works have shown that networks with similar structure, but disparate ionic conductances, can generate similar motor patterns as long as certain conditions are met (Golowasch et al., 1999a, 1999b; Liu et al., 1998; O’Leary et al., 2014; Prinz et al., 2003a; Prinz et al., 2004; Schulz et al., 2006; Taylor et al., 2009). Homeostatic regulatory mechanisms appear to allow individual animals to reach one of many permissive parameter combinations. Some of the pioneering works on homeostatic regulation were performed using isolated and cultured STG neurons: Turrigiano and colleagues (1995) concluded that selective regulation of current densities underlies spontaneous changes in the activity of cultured neurons. Neurons went from being silent or tonically active to generating bursts of action potentials resembling their activity in the intact network by selectively adjusting the magnitudes of their ionic conductances. This not only documented the ability of neurons to compensate for long-lasting changes in their activity by altering their cellular properties, but also built the foundation of a large body of work across many model systems (Turrigiano, 1999).

Ion Channel Co-Regulation

However, ionic conductances do not vary independently. Rather, there is an activity-dependent co-regulation of particular ionic currents (Khorkova & Golowasch, 2007; O’Leary, Williams, Caplan, & Marder, 2013; O’Leary et al., 2014; Temporal et al., 2014), keeping cellular and network parameters within boundaries suitable to support the appropriate output. Dopamine, for example, enables an activity-dependent recovery of the pyloric neuron phase relationships. Acting on low affinity receptors, dopamine decreases the voltage-gated transient outward current (the A-current, IA). As a result, the activity phase of the modulated neuron changes. At the same time, dopamine acts at high affinity receptors of the same neuron to confer activity-dependent reduction of a hyperpolarization-activated inward current (Ih), restoring the IA:Ih ratio and neuronal activity phase (Krenz et al., 2013). Phase is well maintained in the pyloric network, within and across species. Temperature perturbations as well as current injections into the pacemakers, for instance, change the cycle period of the rhythm, but fail to alter the phase relations due to the dynamics of intrinsic conductances such as Ih and IA (Hooper, 1997a, 1997b; Tang et al., 2010). The relative activity patterns of the pyloric neurons are also restored after disruptive perturbations and injury (Davis, 2006; Luther et al., 2003; Thoby-Brisson & Simmers, 1998), an event attributed at least partly to a rearrangement of ionic conductance levels (Faumont et al., 2005; Gansert et al., 2007; Thoby-Brisson & Simmers, 2002). Activity-dependent (Haedo & Golowasch, 2006) and activity-independent homeostatic mechanisms act to maintain network output. An example for the latter is when IA is over expressed by mRNA injection, which by itself should disrupt rhythmic activity, instead results in a compensatory increase of Ih and a preservation of network activity (MacLean et al., 2003). More recently, these experimental findings were followed up by a series of computational studies indicating that simple homeostatic tuning rules can produce conductance correlations and give rise to robust circuit activity (O’Leary et al., 2013).

Neuromodulators Influence Ion Channel Regulation

Whether similar compensatory mechanisms are acting on the gastric mill circuit is unclear, but recent studies have shown that phase relationships in the gastric mill rhythm are quite variable (Hedrich et al., 2009; Yarger & Stein, 2015). Since neuromodulators are important regulators of cellular and network activity, it is not surprising that they are also involved in maintaining and shaping network activity. Khorkova and Golowasch (2007) showed that three voltage-gated ionic currents (IA, Ih and IHTK (high-threshold potassium current)) possess a high degree of correlation in their current density levels in PD neurons. Regardless of decentralization and removal of neuromodulatory input to the STG, Ih and IA were still tightly correlated, although the Ih/IHTK and IA/IHTK pairs were no longer correlated. This loss of the ion current co-dependence was prevented when the peptide modulator proctolin was present. Consequently, neuromodulation may reduce variability. In fact, after decentralization, rhythmic activity can slow or cease. Within a few hours, circuit properties begin to reconfigure, which can be sufficient to recover pyloric activity (Khorkova & Golowasch, 2007). However, the long-term removal of neuromodulators and the subsequent re-configuration of intrinsic properties appear to increase variability of both pyloric and gastric mill activity (Hamood et al., 2015; Hamood & Marder, 2015). A recent study in the lobster STNS indicates that low-level monoaminergic tone acts to permit cell-specific and activity-dependent regulation of ionic conductances (Krenz, Parker, Rodgers, & Baro, 2015), facilitating the maintenance of conductance correlations and reducing variability of activity patterns over the long term.

Networks of similar structure thus can possess a varying complement of ionic conductances while producing similar output patterns. A remarkable conclusion is that this enables individual animals within and across species to select from manifold combinations of conductances in order to modify or maintain activity patterns. In conclusion, it is the balance of conductances, not their absolute values, that defines a given activity feature.

Limits and Future

While theoretical studies indicate that many conductance combinations can, in principle, support the same network activity, there may be factors in biological systems that determine which combinations are viable. Some obvious factors influencing conductance space are the inherent ability to (co-)regulate different ionic conductances, the genetic variability between animals, life-history-dependent effects, or environmental pressures. Because decapod crustaceans are poikilothermic, temperature is a major environmental stressor. Ionic conductances have an intrinsically wide range of Q10 values, that is, they respond very differently to temperature changes. Nevertheless, the pyloric rhythm is faithfully generated over a wide temperature range, with stable phase relationships between neurons in vitro and in vivo (Soofi et al., 2014; Tang et al., 2010; Tang, Taylor, Rinberg, & Marder, 2012). Cellular pacemaker models indicate that again many possible combinations of conductance densities allow the production of robust activity over a range of temperatures (Caplan, Williams, & Marder, 2014), but that conductance space might be limited in that only certain combinations of Q10 values support oscillations. How evolutionary or epigenetic influences may affect network performance and ion channel properties, including Q10 values, is unknown, but might be addressed using genetically similar and modifiable species of crustaceans (Vogt, 2010) whose genome is currently being annotated (Lyko, Stein, unpublished work). Further, neuromodulation may play an important part in conferring robustness to the circuits. The activity levels of the modulatory MCN1, for example, increases with temperature, and the resulting enhancement of peptide-evoked IMI can rescue temperature-induced breakdowns of gastric mill activity. IMI here counteracts a temperature-induced increase in leak currents (Städele, Heigele, & Stein, 2015).

Today, the STG is a test-bed for identifying and studying novel physiological phenomena. Looking forward to understanding the circuit dynamics of larger and less accessible circuits, lessons learned in small motor circuits such as the STG can help identify the issues crucial for circuit analysis in all systems. The system remains an outstanding source for testing theoretical predictions in experiments of proper design and with rigorous controls that would be impossible to carry out without the detailed knowledge of the underlying neural circuits. Since work in the STG is performed in a network context rather than on individual neuron or conductance levels, it will continue to inform us of the impact of cellular and circuit properties on circuit performance. Perhaps the most exciting prospect for future work will be the application of molecular and genetic approaches to study long-term regulation of circuit dynamics.

Acknowledgments

I would like to thank Jessica Ausborn, Margaret Demaegd, Chris Goldsmith, and Carola Städele for commenting on earlier versions of the manuscript. I would also like to thank Farzan Nadim, Mike Nusbaum, and the neuroethology community for their helpful suggestions.

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