Neuromodulation and flexibility in Central Pattern Generator networks

Abstract

Central Pattern Generator (CPG) networks, which organize rhythmic movements, have long served as models for neural network organization. Modulatory inputs are essential components of CPG function: neuromodulators set the parameters of CPG neurons and synapses to render the networks functional. Each modulator acts on the network by many effects which may oppose one another; this may serve to stabilize the modulated state. Neuromodulators also determine the active neuronal composition in the CPG, which varies with state changes such as locomotor speed. The pattern of gene expression which determines the electrophysiological personality of each CPG neuron is also under modulatory control. It is not possible to model the function of neural networks without including the actions of neuromodulators.

Figure 1. Positive and negative effects of neuromodulators in a CPG network

A: Simultaneous recordings from 4 neurons in the pyloric network of the lobster stomatogastric ganglion, showing the rhythmic pattern under normal conditions of modulatory inputs. AB: Anterior Burster; PD: Pyloric Dilator; LP: Lateral Pyloric; PY: Pyloric Constrictor. B: Changes in the pyloric motor pattern when dopamine (10^-4M) is added. The AB, LP and PY neurons are all excited and fire more strongly while the PD is inhibited and fires weakly or not at all. There are also significant phasing changes. C: Wiring diagram of the pyloric network, showing the effects of dopamine on ionic currents. Inhibitory synapses are drawn with filled circles; non-rectifying electrical synapses are drawn with a resistor, while rectifying synapses are drawn with a diode symbol indicating the direction of preferred current flow. Neurons in green are excited by dopamine, while those in red are inhibited. Dopamine evokes different changes in ionic currents in each of the neurons. Modulated currents in red would cause opposite effects on neuronal firing than the overall effect of dopamine. D: Summary of dopamine’s effects on synaptic transmission in the pyloric network. Strengthened and weakened synapses are shown with thick and dashed lines, respectively. Some electrical synapses show opposite responses to dopamine depending on the direction of current flow, indicated by synapses that are partly bold and partly dashed. Plus and minus symbols in nerve terminals reflect changes in voltage-activated pre-synaptic calcium accumulation during dopamine. Pipette symbols with plus and minus symbols indicate changes in post-synaptic responsiveness to iontophoresed glutamate, the transmitter of most of the pyloric neurons. Circled plus and minus symbols in the cell bodies indicate dopamine’s effect on post-synaptic input resistance. Red-circled synapses are those where the pre-synaptic effects of dopamine are of opposing sign to its post-synaptic effects. Modified from Ref. .

Figure 2. Speed-dependent locomotor recruitment and inhibition of V2a interneurons in the zebrafish spinal cord

The CiD interneurons show a ventrodorsal gradient of activity at different speeds during locomotion. A: Dorsal CiD is activated at the beginning of a swim bout, when the cycle frequency is highest, and is inactive at lower frequencies later in the swim bout. The recordings in the middle show the intracellular recording from the CiD (top) and an extracellular recording from a motor nerve to monitor the swim frequency (bottom). The histogram at right shows the percentage of dorsal CiDs active at different swim frequencies. B: Ventral CiD is silent and actively inhibited at the highest frequencies at the beginning of the swim bout, but is activated at lower swim frequencies. Histogram at right shows that ventral CiDs are active at lower frequencies than the dorsal CiD neurons. Modified from Ref. . C: Summary diagram of recruitment of interneurons at different swim speeds in the larval zebrafish. Modified from Ref. .

Figure 3. Ionic current pair coregulation helps to define identities of neurons

A: Variations in RNA expression levels in single identified neurons from the lobster stomatogastric ganglion. Each graph shows the relationship between the number of copies of RNA for two ion channel genes, measured in multiple copies of a number of different identified neurons. The lines show the average slope of the relationship. Note that no neuron type has a linear relationship between all the current pairs shown; different neurons show different subsets of relationships. Note also that the slope of the a current relationship can differ strongly between neurons. This helps define the electrophysiological properties of each neuron type. From Ref. . B: Modeling the relationship between levels of expression of the slow calcium current, g_CaS and the calcium-activated potassium current, g_KCa, in a database of bursting neuron models. Each current’s maximal conductance was allowed to vary over 6 values, shown on the axes. The database was polled to determine the current levels in many bursting models with duty cycles between 0.1 and 0.2. The color code shows the numbers of models with each correlation; clearly there is a linear relationship between these currents, similar to those found experimentally in A. C: Variation in the current correlations in bursting models as the criteria for inclusion are sequentially restricted. Bottom row: Grey boxes show the correlations between currents in the database models of bursting with the indicated parameters. Then pairs of parameters are combined to identify model neurons that combine both of the lower parameters. This process is repeated going up the figure. At each level, the correlations found in the parent pairs are shown in grey boxes, and if they are blank, there is no correlation at the next level. This shows that as the selected set becomes increasingly constrained by multiple parameters, the numbers of ion channel correlations changes in unexpected ways, but typically decreases. B and C from ref. .