Neural input and neural control of the subcommissural organ.
نویسندگان
چکیده
The neural control of the subcommissural organ (SCO) has been partially characterized. The best known input is an important serotonergic innervation in the SCO of several mammals. In the rat, this innervation comes from raphe nuclei and appears to exert an inhibitory effect on the SCO activity. A GABAergic innervation has also been shown in the SCO of the rat and frog Rana perezi. In the rat, GABA and the enzyme glutamate decarboxylase are involved in the SCO innervation. GABA is taken up by some secretory ependymocytes and nerve terminals, coexisting with serotonin in a population of synaptic terminals. Dopamine, noradrenaline, and different neuropeptides such as LH-RH, vasopressin, vasotocin, oxytocin, mesotocin, substance P, alpha-neoendorphin, and galanin are also involved in SCO innervation. In the bovine SCO, an important number of fibers containing tyrosine hydroxylase are present, indicating that in this species dopamine and/or noradrenaline-containing fibers are an important neural input. In Rana perezi, a GABAergic innervation of pineal origin could explain the influence of light on the SCO secretory activity in frogs. A general conclusion is that the SCO cells receive neural inputs from different neurotransmitter systems. In addition, the possibility that neurotransmitters and neuropeptides present in the cerebrospinal fluid may also affect the SCO activity, is discussed.
منابع مشابه
Neural Controller Design for Suspension Systems
The main problem of vehicle vibration comes from road roughness. An active suspension systempossesses the ability to reduce acceleration of sprung mass continuously as well as to minimizesuspension deflection, which results in improvement of tire grip with the road surface. Thus, braketraction control and vehicle maneuverability can be improved consider ably .This study developeda new active su...
متن کاملIdentification of Multiple Input-multiple Output Non-linear System Cement Rotary Kiln using Stochastic Gradient-based Rough-neural Network
Because of the existing interactions among the variables of a multiple input-multiple output (MIMO) nonlinear system, its identification is a difficult task, particularly in the presence of uncertainties. Cement rotary kiln (CRK) is a MIMO nonlinear system in the cement factory with a complicated mechanism and uncertain disturbances. The identification of CRK is very important for different pur...
متن کاملMulti-Step-Ahead Prediction of Stock Price Using a New Architecture of Neural Networks
Modelling and forecasting Stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. This nonlinearity affects the efficiency of the price characteristics. Using an Artificial Neural Network (ANN) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...
متن کاملNumerical and Neural Network Modeling and control of an Aircraft Propeller
In this paper, parametric and numerical model of the DC motor, connected to aircraft propellers are extracted. This model is required for controlling trust and velocity of the propellers, and consequently, an aircraft. As a result, both of torque and speed of the propeller can be controlled simultaneously which increases the kinematic and kinetic performance of the aircraft. Parametric model of...
متن کاملDynamic Sliding Mode Control of Nonlinear Systems Using Neural Networks
Dynamic sliding mode control (DSMC) of nonlinear systems using neural networks is proposed. In DSMC the chattering is removed due to the integrator which is placed before the input control signal of the plant. However, in DSMC the augmented system is one dimension bigger than the actual system i.e. the states number of augmented system is more than the actual system and then to control of such ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Microscopy research and technique
دوره 52 5 شماره
صفحات -
تاریخ انتشار 2001