Neuronal mechanisms underlying the facilitatory control of uropod steering behaviour during treadmill walking in crayfish. I. Antagonistically regulated background excitability of uropod motoneurones

Functional significance of passive and active dendritic properties in the synaptic integration by an identified nonspiking interneuron of crayfish

Nonspiking interneurons control their synaptic output directly by membrane potential changes caused by synaptic activities. Although these interneurons do not generate spikes, their dendritic membrane is endowed with a variety of voltage-dependent conductances whose functional significance in synaptic integration remains unknown. We quantitatively investigated how the passive and active dendritic properties affect the synaptic integration in an identified nonspiking interneuron of crayfish by computer simulation using its multicompartment model based on electrophysiological measurements and three-dimensional morphometry. At the resting potential level, the attenuation factor (V(s)/V(t)) of a unitary synaptic potential in the course of its spread from a dendritic terminal (V(s)) to other terminals (V(t)) ranged from 4.42 to 6.30 with no substantial difference between hyperpolarizing and depolarizing potentials. The compound synaptic responses to strong mechanosensory stimulation could be reproduced in calculation only as the result of spatial summation of attenuated potentials, not as any single large potential. The characteristic response could be reproduced by assuming that the active conductances were distributed only in the dendritic region where the synaptic summation was carried out. The active conductances in other parts of the cell affected neither the shape of the compound synaptic response nor the dendritic spread of synaptic potentials. These findings suggest that the active membrane conductances do not affect the spatial distribution of synaptic potentials over dendrites but function in sculpting the summed synaptic potential to enhance temporal resolution in the synaptic output of the nonspiking interneuron.

Information processing by nonspiking interneurons: passive and active properties of dendritic membrane determine synaptic integration

Nonspiking interneurons control activities of postsynaptic cells without generating action potentials in the central nervous system of many invertebrates. Physiological characteristics of their dendritic membrane have been analyzed in previous studies using single electrode current- and voltage-clamp techniques. We constructed a single compartment model of an identified nonspiking interneuron of crayfish. Experimental results allowed us to simulate how the passive and active properties of the dendritic membrane influence the integrative processing of synaptic inputs. The results showed that not only the peak amplitude but also the time course of synaptic potentials were dependent on the membrane potential level at which the synaptic activity was evoked. When the synaptic input came sequentially, each individual input was still discernible at depolarized levels at which the membrane time constant was short due to depolarization-dependent membrane conductances. In contrast, synaptic potentials merged with each other to develop a sustained potential at hyperpolarized levels where the membrane behaved passively. Thus, synaptic integration in a single nonspiking interneuron depends on the value of membrane potential at which it occurs. This probably reflects the temporal resolution required for specific types of information processing.

Neural adaptation in the generation of rhythmic behavior

Motor systems can adapt rapidly to changes in external conditions and to switching of internal goals. They can also adapt slowly in response to training, alterations in the mechanics of the system, and any changes in the system resulting from injury. This article reviews the mechanisms underlying short- and long-term adaptation in rhythmic motor systems. The neuronal networks underlying the generation of rhythmic motor patterns (central pattern generators; CPGs) are extremely flexible. Neuromodulators, central commands, and afferent signals all influence the pattern produced by a CPG by altering the cellular and synaptic properties of individual neurons and the coupling between different populations of neurons. This flexibility allows the generation of a variety of motor patterns appropriate for the mechanical requirements of different forms of a behavior. The matching of motor output to mechanical requirements depends on the capacity of pattern-generating networks to adapt to slow changes in body mechanics and persistent errors in performance. Afferent feedback from body and limb proprioceptors likely plays an important role in driving these long-term adaptive processes.