The complexity of small circuits: the stomatogastric nervous system

Abstract

The crustacean stomatogastric nervous system is a long-standing test bed for studies of circuit dynamics and neuromodulation. We give a brief update on the most recent work on this system, with an emphasis on the broader implications for understanding neural circuits. In particular, we focus on new findings underlining that different levels of dynamics taking place at different time scales all interact in multiple ways. Dynamics due to synaptic and intrinsic neuronal properties, neuromodulation, and long-term gene expression-dependent regulation are not independent, but influence each other. Extensive research on the stomatogastric system shows that these dynamic interactions convey robustness to circuit operation, while facilitating the flexibility of producing multiple circuit outputs.

Figure 1

The pyloric and gastric mill central pattern generating circuits of the stomatogastric ganglion. A: Schematic of the isolated STNS. The STG contains the pyloric and gastric mill circuits. The commissural ganglia (CoG) contain the cell bodies of projection neurons like the modulatory commisural neuron 1 (MCN1), which project to the neuropil of the STG. B: The core pyloric and gastric mill circuit diagrams. Not all cells types and synapses are shown. Inhibitory chemical synapses are shown as circles, electrical coupling as resistor symbols, and excitatory inputs from MCN1 as triangles. Rhythm generation is based on intrinsic oscillatory properties of the pacemaker kernel in the pyloric circuit, and on reciprocal inhibitory connections between non-oscillatory neurons (half-center) in the gastric mill circuit. Note that both circuits are interconnected by direct synapses and through feedback to the terminals of projection neurons. C: The typical tri-phasic pyloric pattern. In each cycle, a pacemaker burst is followed by neurons burst in two different phases, in rebound from pacemaker inhibition. D: The bi-phasic gastric mill rhythm is often not spontaneously active, but can be activated by stimulating modulatory projection neurons like MCN1. Note that the interconnection between both circuits leads to substantial pyloric modulation of the much slower gastric mill neuron bursting. The pyloric pacemaker neuron AB is shown as a reference for pyloric timing. A, B, & D are modified from reference [9]; C is modified from reference [8].

Figure 2

Different organizing principles underlying circuit modulation by biogenic amines and neuropeptides. A: In different cell types, activation of dopamine receptors (DAR) can affect the gating properties of different subsets of ion channels, and the effects can have a different sign. Ion channels giving rise to inward currents are shown in yellow, and those giving rise to outward currents in blue. B: The sum effects of the diverse cellular loci of dopamine actions are functional enhancements (green) or recuctions (red) of excitability in all pyloric neurons and strength of all pyloric synapses. C: Neuropeptide modulation affects a limited number of intracellular targets. Different neuropeptides all converge on the same voltage-gated inward current (I_MI), but different cell types respond to a different subset of neuropeptides. RPCH: red pigment concentrating hormone; CabTRP: Cancer borealis tachykinin-related peptide; CCAP: crustacean cardioactive peptide; proc: proctolin. D: Despite the convergence of neuropeptide effects on the same subcellular target, the different subsets of circuit neurons affected by each neuropetide give rise to divergent effects on circuit activity. A & B are modified from reference [16], C & D are modified from references [27] and [32].

Figure 3

Schematic of interactions of different regulatory mechanisms that affect circuit operation at different time scales. At the fast time scale, neuronal and synaptic properties give rise to circuit activity. At the intermediate time scale, neuromodulators convey flexibility, as different neuromodulators can tune neuronal and synaptic properties to generate different circuit outputs. Circuit activity itself can shape input patterns from modulatory neurons through feedback connections. At the slow time scale, long-term regulatory mechanisms dependent on neuronal activity and the presence of neuromodulators convey stability of circuit output and prevent circuit crashes.