2006 年 65 巻 1 号 p. 1-10
The vestibular nucleus receives sensory signals from the vestibular periphery as well as visual and proprioceptive information, and then sends afferents to regions controlling posture and ocular movements. In this network, the vestibular nucleus neurons play a role in not only relaying signals or switching between excitatory or inhibitory transmission, but also modifying the signals by their intrinsic membrane properties. Since the late 80's, in vitro electrophysiological recording in rodent brainstem slices has revealed detailed neuronal membrane properties.
Medial vestibular nucleus neurons (MVNn) were classified into types A and B by the shape of their action potential (AP) and after-hyperpolarization (AHP). Type A exhibits a broad AP followed by a single, deep AHP, and shows transient rectification ascribed to A-like currents. Type B exhibits a narrow AP followed by a double (an early fast and a delayed slow) AHP. In some type B MVNn, a prolonged plateau potential or a low-threshold calcium spike (LTS) can be evoked. Firing responses of MVNn to step, ramp, or sinusoidal current modulation indicated that type B neurons have more kinetic characters than type A showing tonic responses.
In electrophysiological studies using slice preparations, since most of the afferents and efferents were terminated, the property of each individual neuron was difficult to link to the role in the entire network. However, recent studies using in vitro whole brain preparation that preserves the root of cranial nerves and the neural circuits in the brain, a combination of whole-cell patch clamp and RT-PCR techniques that can identify the neuronal transmitter, or molecular techniques like fluorescent promoter gene transfer into specific areas leading to the identification of the afferent origin of the recorded neuron, are revealing the function of intrinsic membrane properties of neurons in the network. Realistic neuron models based on physiological results have also been developed, which provides an other way to understand single neuron properties in relation to the whole network.