scholarly journals Sensory Regulation of Network Components Underlying Ciliary Locomotion in Hermissenda

2008 ◽  
Vol 100 (5) ◽  
pp. 2496-2506 ◽  
Author(s):  
Terry Crow ◽  
Lian-Ming Tian
Keyword(s):  
Neural Network ◽  
Spike Activity ◽  
Electrical Coupling ◽  
Rhythmic Activity ◽  
Burst Activity ◽  
Type I ◽  
Sensory Regulation ◽  
The Neural Network ◽  
Efferent Neurons ◽  
Ciliary Locomotion

Ciliary locomotion in the nudibranch mollusk Hermissenda is modulated by the visual and graviceptive systems. Components of the neural network mediating ciliary locomotion have been identified including aggregates of polysensory interneurons that receive monosynaptic input from identified photoreceptors and efferent neurons that activate cilia. Illumination produces an inhibition of type Ii (off-cell) spike activity, excitation of type Ie (on-cell) spike activity, decreased spike activity in type IIIi inhibitory interneurons, and increased spike activity of ciliary efferent neurons. Here we show that pairs of type Ii interneurons and pairs of type Ie interneurons are electrically coupled. Neither electrical coupling or synaptic connections were observed between Ie and Ii interneurons. Coupling is effective in synchronizing dark-adapted spontaneous firing between pairs of Ie and pairs of Ii interneurons. Out-of-phase burst activity, occasionally observed in dark-adapted and light-adapted pairs of Ie and Ii interneurons, suggests that they receive synaptic input from a common presynaptic source or sources. Rhythmic activity is typically not a characteristic of dark-adapted, light-adapted, or light-evoked firing of type I interneurons. However, burst activity in Ie and Ii interneurons may be elicited by electrical stimulation of pedal nerves or generated at the offset of light. Our results indicate that type I interneurons can support the generation of both rhythmic activity and changes in tonic firing depending on sensory input. This suggests that the neural network supporting ciliary locomotion may be multifunctional. However, consistent with the nonmuscular and nonrhythmic characteristics of visually modulated ciliary locomotion, type I interneurons exhibit changes in tonic activity evoked by illumination.

scholarly journals Network interneurons underlying ciliary locomotion in Hermissenda

2013 ◽  
Vol 109 (3) ◽  
pp. 640-648 ◽  
Author(s):  
Terry Crow ◽  
Nan Ge Jin ◽  
Lian-Ming Tian
Keyword(s):  
Spike Activity ◽  
Excitatory Postsynaptic Potential ◽  
Type I ◽  
Type Ii ◽  
Visually Guided ◽  
Monosynaptic Connection ◽  
Postsynaptic Potential ◽  
The Neural Network ◽  
Efferent Neurons ◽  
Ciliary Locomotion

In the nudibranch mollusk Hermissenda, ciliary locomotion contributes to the generation of two tactic behaviors. Light elicits a positive phototaxis, and graviceptive stimulation evokes a negative gravitaxis. Two classes of light-responsive premotor interneurons in the network contributing to ciliary locomotion have been recently identified in the cerebropleural ganglia. Aggregates of type I interneurons receive monosynaptic excitatory (Ie) or inhibitory (Ii) input from identified photoreceptors. Type II interneurons receive polysynaptic excitatory (IIe) or inhibitory (IIi) input from photoreceptors. The ciliary network also includes type III inhibitory (IIIi) interneurons, which form monosynaptic inhibitory connections with ciliary efferent neurons (CENs). Illumination of the eyes evokes a complex inhibitory postsynaptic potential, a decrease of Ii spike activity, a complex excitatory postsynaptic potential, and an increase of Ie spike activity. Here, we characterized the contribution of identified I, II, and IIIi interneurons to the neural network supporting visually guided locomotion. In dark-adapted preparations, light elicited an increase in the tonic spike activity of IIe interneurons and a decrease in the tonic spike activity of IIi interneurons. Fluorescent dye-labeled type II interneurons exhibited diverse projections within the circumesophageal nervous system. However, a subclass of type II interneurons, IIe(cp) and IIi(cp) interneurons, were shown to terminate within the ipsilateral cerebropleural ganglia and indirectly modulate the activity of CENs. Type II interneurons form monosynaptic or polysynaptic connections with previously identified components of the ciliary network. The identification of a monosynaptic connection between Ie and IIIi interneurons shown here suggest that they provide a major role in the light-dependent modulation of CEN spike activity underlying ciliary locomotion.

scholarly journals Heterogeneous convolutional neural networks to build movement routes of the objects in spatial environment with accumulated energy potential

2021 ◽  
Vol 16 (93) ◽  
pp. 21-37
Author(s):  
Yuriy N. Lavrenkov ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Data Processing ◽  
Optimal Path ◽  
Rhythmic Activity ◽  
Main Role ◽  
Energy Potential ◽  
Convolutional Network ◽  
The Neural Network ◽  
Neural Populations

We consider the synthesis of a hybrid neural convolutional network with the modular topology-based architecture, which allows to arrange a parallel convolutional computing system to combine both the energy transfer and data processing, in order to simulate complex functions of natural biological neural populations. The system of interlayer neural commutation, based on the distributed resonance circuits with the layers of electromagnetic metamaterial between the inductive elements, is a base for simulation of the interaction between the astrocyte networks and the neural clusters responsible for information processing. Consequently, the data processing is considered both at the level of signal transmission through neural elements, and as interaction of artificial neurons and astrocytic networks ensuring their functioning. The resulting two-level neural system of data processing implements a set of measures to solve the issue based on the neural network committee. The specific arrangement of the neural network enables us to implement and configure the educational procedure using the properties absent in the neural networks consisting of neural populations only. The training of the convolutional network is based on a preliminary analysis of rhythmic activity, where artificial astrocytes play the main role of interneural switches. The analysis of the signals moving through the neural network enables us to adjust variable components to present information from training bunches in the available memory circuits in the most efficient way. Moreover, in the training process we observe the activity of neurons in various areas to evenly distribute the computational load on neural network modules to achieve maximum performance. The trained and formed convolutional network is used to solve the problem of determining the optimal path for the object moving due to the energy from the environment

Modeling and Optimizing Boiler Design using Neural Network and Firefly Algorithm

2018 ◽  
Vol 27 (3) ◽  
pp. 393-412 ◽  
Author(s):  
Sangram Bhagwanrao Savargave ◽  
Madhukar Jagannath Lengare
Keyword(s):  
Neural Network ◽  
Firefly Algorithm ◽  
Modeling Technique ◽  
Type I ◽  
Boiler Plant ◽  
Statistical Measures ◽  
Emissions Modeling ◽  
The Neural Network ◽  
Boiler Design ◽  
Non Linear System

Abstract The significance of researches in modeling of boiler design and its optimization is high for saving energy and minimizing emissions. Modeling the boiler plant with all demands is rather challenging. A lot of techniques are reported in the literature for enhancing the boiler efficiency. The neural network scheme has been proved for the boiler design, and it provides a framework for the non-linear system models. In this paper, a hybrid of artificial neural network and firefly algorithm is proposed. The proposed modeling technique is simulated in MATLAB, and the experimentation is carried out extensively. The performance of the proposed modeling technique is demonstrated using type I and II error functions, followed by performing higher statistical measures such as error deviation and correlation analysis. Comparative analysis is made to substantiate the superiority of the proposed modeling technique.

scholarly journals Polysensory Interneuronal Projections to Foot Contractile Pedal Neurons in Hermissenda

2009 ◽  
Vol 101 (2) ◽  
pp. 824-833 ◽  
Author(s):  
Terry Crow ◽  
Lian-Ming Tian
Keyword(s):  
Neural Circuit ◽  
Behavioral Responses ◽  
Neural Circuitry ◽  
Type I ◽  
Synaptic Connections ◽  
Pedal Ganglion ◽  
Conditioning Procedure ◽  
Efferent Neurons ◽  
Conditioned Responses ◽  
Ciliary Locomotion

A Pavlovian-conditioning procedure may produce modifications in multiple behavioral responses. As an example, conditioning may result in the elicitation of a specific somatomotor conditioned response (CR) and, in addition, other motor and visceral CRs. In the mollusk Hermissenda conditioning produces two conditioned responses: foot-shortening and decreased locomotion. The neural circuitry supporting ciliary locomotion is well characterized, although the neural circuit underlying foot-shortening is poorly understood. Here we describe efferent neurons in the pedal ganglion that produce contraction or extension of specific regions of the foot in semi-intact preparations. Synaptic connections between polysensory type Ib and type Is interneurons and identified foot contractile efferent neurons were examined. Type Ib and type Is interneurons receive synaptic input from the visual, graviceptive, and somatosensory systems. Depolarization of type Ib interneurons evoked spikes in identified tail and lateral foot contractile efferent neurons. Mechanical displacement of the statocyst evoked complex excitatory postsynaptic potentials (EPSPs) and spikes recorded from type Ib and type Is interneurons and complex EPSPs and spikes in identified foot contractile efferent neurons. Depolarization of type Ib interneurons in semi-intact preparations produced contraction and shortening along the rostrocaudal axis of the foot. Depolarization of Is interneurons in semi-intact preparations produced contraction of the anterior region of the foot. Taken collectively, the results suggest that type Ib and type Is polysensory interneurons may contribute to the neural circuit underlying the foot-shortening CR in Hermissenda.

Neural Network for Automatic Analysis of Motility Data

1994 ◽  
Vol 33 (01) ◽  
pp. 157-160 ◽  
Author(s):  
S. Kruse-Andersen ◽  
J. Kolberg ◽  
E. Jakobsen
Keyword(s):  
Neural Network ◽  
Muscular Activity ◽  
Recording System ◽  
Automatic Analysis ◽  
Biologically Relevant ◽  
System A ◽  
The Neural Network ◽  
Valuable Method ◽  
Motor Abnormalities ◽  
Trained Network

Abstract:Continuous recording of intraluminal pressures for extended periods of time is currently regarded as a valuable method for detection of esophageal motor abnormalities. A subsequent automatic analysis of the resulting motility data relies on strict mathematical criteria for recognition of pressure events. Due to great variation in events, this method often fails to detect biologically relevant pressure variations. We have tried to develop a new concept for recognition of pressure events based on a neural network. Pressures were recorded for over 23 hours in 29 normal volunteers by means of a portable data recording system. A number of pressure events and non-events were selected from 9 recordings and used for training the network. The performance of the trained network was then verified on recordings from the remaining 20 volunteers. The accuracy and sensitivity of the two systems were comparable. However, the neural network recognized pressure peaks clearly generated by muscular activity that had escaped detection by the conventional program. In conclusion, we believe that neu-rocomputing has potential advantages for automatic analysis of gastrointestinal motility data.

Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire

1997 ◽  
Vol 36 (04/05) ◽  
pp. 349-351
Author(s):  
H. Mizuta ◽  
K. Kawachi ◽  
H. Yoshida ◽  
K. Iida ◽  
Y. Okubo ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Psychiatric Diagnosis ◽  
Psychiatric Patients ◽  
Bayesian Classifier ◽  
Neural Network Classifier ◽  
Correct Decision ◽  
Neurotic Disorders ◽  
The Neural Network

Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

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