Adaptive motor control in crayfish

International audienceThis article reviews the principles that rule the organization of motor commands that have been described over the past ®ve decades in cray®sh. The adaptation of motor behaviors requires the integration of sensory cues into the motor command. The respective roles of central neural networks and sensory feedback are presented in the order of increasing complexity. The simplest circuits described are those involved in the control of a single joint during posture (negative feedback±resistance re¯ex) and movement (modulation of sensory feedback and reversal of the re¯ex into an assistance re¯ex). More complex integration is required to solve problems of coordination of joint movements in a pluri-segmental appendage, and coordination of dierent limbs and dierent motor systems. In addition, beyond the question of mechanical ®tting, the motor command must be appropriate to the behavioral context. Therefore, sensory information is used also to select adequate motor programs. A last aspect of adaptability concerns the possibility of neural networks to change their properties either temporarily (such on-line modulation exerted, for example, by presynaptic mechanisms) or more permanently (such as plastic changes that modify the synaptic ecacy). Finally, the question of how``automatic'' local component networks are controlled by descending pathways, in order to achieve behaviors, is discussed.

Physiological and morphological analysis of a coordinating circuit

During swimming the four paired swimmerets on the crayfish’s abdomen are coordinated in an anteriorly proceeding metachronal wave with a phase lag of 23 ± 7% between each segment. Each swimmeret is innervated by motor neurons which are driven by local interneurons of the central pattern generator (CPG). The intersegmental coordination of the CPGs is achieved by three neurons located in each hemisegment that form a coordinating circuit. One ascending (ASCE) and one descending (DSC) Coordinating Neuron encode the information about the status of their home module and project it to other ganglia. A nonspiking neuron, Commissural Interneuron 1 (ComInt1), decodes this information transmitted by three Coordinating Neurons with a gradient of synaptic strength. The largest excitatory postsynaptic potential (EPSP) is elicited by the directly adjacent Coordinating Neurons, the smallest of the Coordinating Neurons whose origin is the most distant. Thereby, EPSPs elicited by ASCE are always larger than those of DSC. Coordinating Neurons adapt to the system’s excitation level by tuning their encoding properties, so that large differences in burst strength are encoded by a narrow range of spikes. This finding led to the hypothesis that ComInt1 also adapts to the level of excitation by similarly tuning its decoding abilities. Therefore, I recorded intracellularly from ComInt1 and changed the excitation level by bath application of carbachol (CCh; cholinergic agonist), crustacean cardioactive peptide (CCAP, muscarinic agonist), or edrophonium chloride (EdCl; acetylcholine esterase inhibitor). To investigate direct and indirect actions of the drugs, I analyzed ComInt1’s membrane oscillations and its EPSP shapes, resulting in the C1 intensity. Moreover, I analyzed its membrane potential changes and measured input resistance with the network intact and in the isolated neuron. ComInt1 adapts to the excitation level of its own CPG. Moreover, ComInt1 continuously samples the activity of its own microcircuit via an electrical synapse and receives perturbations transmitted via chemical synapses from the Coordinating Neurons. Therefore, it is capable to decode and to integrate information of the other three CPGs and to detect mismatches between the states of activity of all four ipsilateral coupled oscillators to synchronize those activities to each other. Secondly, I investigated how the gradient of synaptic strength in ComInt1 is achieved. I hypothesized that the different sized EPSPs are due to differences in the number of synapses or in the size of synapses the Coordinating Neurons form onto ComInt1. Therefore, I iontophoretically filled ComInt1 and single Coordinating Neurons with fluorescence dyes and immunohistochemically labeled presynaptic boutons of Coordinating Neurons with Anti-Synapsin. I identified synapses of Coordinating Neurons at the dorsal midline region, where ComInt1 has one ascending and descending dendritic branch. Moreover, the axons of the Coordinating Neurons pass through the other ganglia in this region. I calculated the volume of the colocalized areas of dye-filled Coordinating Neurons and immunohistochemically labeled presynaptic boutons, which provided the first evidence that the gradient of synaptic strengths has its origin in the synaptic composition

Encoding of Coordinating Information in a Network of Coupled Oscillators

Animal locomotion is driven by cyclic movements of the body or body appendages. These movements are under the control of neural networks that are driven by central pattern generators (CPG). In order to produce meaningful behavior, CPGs need to be coordinated. The crayfish swimmeret system is a model to investigate the coordination of distributed CPGs. Swimmerets are four pairs of limbs on the animal’s abdomen, which move in cycles of alternating power-strokes and return-strokes. The swimmeret pairs are coordinated in a metachronal wave from posterior to anterior with a phase lag of approximately 25% between segments. Each swimmeret is controlled by its own neural microcircuit, located in the body segment’s hemiganglion. Three neurons per hemiganglion are necessary and sufficient for the 25% phase lag. ASCE DSC encode information about their home ganglion’s activity state and send it to their anterior or posterior target ganglia, respectively. ComInt 1, which is electrically coupled to the CPG, receives the coordinating information. The isolated abdominal ganglia chain reliably produces fictive swimming. Motor burst strength is encoded by the number of spikes per ASCE and DSC burst. If motor burst strength varies spontaneously, the coordinating neurons track these changes linearly. The neurons are hypothesized to adapt their spiking range to the occurring motor burst strengths. One aim of this study was to investigate the putative adaptive encoding of the coordinating neurons in electrophysiological experiments. This revealed that the system’s excitation level influenced both the whole system and the individual coordinating neurons. These mechanisms allowed the coordinating neurons to adapt to the range of burst strengths at any given excitation level by encoding relative burst strengths. The second aim was to identify the transmitters of the coordinating neurons at the synapse to ComInt 1. Immunohistochemical experiments demonstrated that coordinating neurons were not co-localized with serotonin-immunoreactive positive neurons. MALDI-TOF mass spectrometry suggested acetylcholine as presumable transmitter

Mechanisms of the Coregulation of Multiple Ionic Currents for the Control of Neuronal Activity

An open question in contemporary neuroscience is how neuromodulators coregulate multiple conductances to maintain functional neuronal activity. Neuromodulators enact changes to properties of biophysical characteristics, such as the maximal conductance or voltage of half-activation of an ionic current, which determine the type and properties of neuronal activity. We apply dynamical systems theory to study the changes to neuronal activity that arise from neuromodulation. Neuromulators can act on multiple targets within a cell. The coregulation of mulitple ionic currents extends the scope of dynamic control on neuronal activity. Different aspects of neuronal activity can be independently controlled by different currents. The coregulation of multiple ionic currents provides precise control over the temporal characteristics of neuronal activity. Compensatory changes in multiple ionic currents could be used to avoid dangerous dynamics or maintain some aspect of neuronal activity. The coregulation of multiple ionic currents can be used as bifurcation control to ensure robust dynamics or expand the range of coexisting regimes. Multiple ionic currents could be involved in increasing the range of dynamic control over neuronal activity. The coregulation of multiple ionic currents in neuromodulation expands the range over which biophysical parameters support functional activity