Coordinated Excitatory Effect of GABAergic Interneurons on Three Feeding Motor Programs in the Mollusk Clione limacina

2005 ◽  
Vol 93 (1) ◽  
pp. 305-315 ◽  
Author(s):  
Tigran P. Norekian ◽  
Aleksey Y. Malyshev
Keyword(s):  
Neural Networks ◽  
Feeding Behavior ◽  
Prey Capture ◽  
Order System ◽  
Dependent Manner ◽  
Rhythmic Movements ◽  
Excitatory Effect ◽  
Double Labeling ◽  
The Neural Networks ◽  
Clione Limacina

Coordination between different motor centers is essential for the orderly production of all complex behaviors. Understanding the mechanisms of such coordination during feeding behavior in the carnivorous mollusk Clione limacina is the main goal of the current study. A bilaterally symmetrical interneuron identified in the cerebral ganglia and designated Cr-BM neuron produced coordinated activation of neural networks controlling three main feeding structures: prey capture appendages called buccal cones, chitinous hooks used for prey extraction from the shell, and the toothed radula. The Cr-BM neuron produced strong excitatory inputs to motoneurons controlling buccal cone protraction. It also induced a prominent activation of the neural networks controlling radula and hook rhythmic movements. In addition to the overall activation, Cr-BM neuron synaptic inputs to individual motoneurons coordinated their activity in a phase-dependent manner. The Cr-BM neuron produced depolarizing inputs to the radula protractor and hook retractor motoneurons, which are active in one phase, and hyperpolarizing inputs to the radula retractor and hook protractor motoneurons, which are active in the opposite phase. The Cr-BM neuron used GABA as its neurotransmitter. It was found to be GABA-immunoreactive in the double-labeling experiments. Exogenous GABA mimicked the effects produced by Cr-BM neuron on the postsynaptic neurons. The GABA antagonists bicuculline and picrotoxin blocked Cr-BM neuron-induced PSPs. The prominent coordinating effect produced by the Cr-BM neuron on the neural networks controlling three major elements of the feeding behavior in Clione suggests that this interneuron is an important part of the higher-order system for the feeding behavior.

Whole body withdrawal circuit and its involvement in the behavioral hierarchy of the mollusk Clione limacina

1996 ◽  
Vol 75 (2) ◽  
pp. 529-537 ◽  
Author(s):  
T. P. Norekian ◽  
R. A. Satterlie
Keyword(s):  
Feeding Behavior ◽  
Motor Neurons ◽  
Prey Capture ◽  
The Body ◽  
Whole Body ◽  
High Threshold ◽  
Behavioral Repertoire ◽  
Withdrawal Behavior ◽  
Cerebral Neurons ◽  
Clione Limacina

1. The behavioral repertoire of the holoplanktonic pteropod mollusk Clione limacina includes a few well-defined behaviors organized in a priority sequence. Whole body withdrawal takes precedence over slow swimming behavior, whereas feeding behavior is dominant over withdrawal. In this study a group of neurons is described in the pleural ganglia, which controls whole body withdrawal behavior in Clione. Each pleural withdrawal (Pl-W) neuron has a high threshold for spike generation and is capable of inducing whole body withdrawal in a semi-intact preparation: retraction of the body-tail, wings, and head. Each Pl-W neuron projects axons into the main central nerves and innervates all major regions of the body. 2. Stimulation of Pl-W neurons produces inhibitory inputs to swim motor neurons that terminate swimming activity in the preparation. In turn, Pl-W neurons receive inhibitory inputs from the cerebral neurons involved in the control of feeding behavior in Clione, neurons underlying extrusion of specialized prey capture appendages. Thus it appears that specific inhibitory connections between motor centers can explain the dominance of withdrawal behavior over slow swimming and feeding over withdrawal in Clione.

Pharmacologically induced elements of the hunting and feeding behavior in the pteropod mollusk Clione limacina. I. Effects of GABA

1993 ◽  
Vol 69 (2) ◽  
pp. 512-521 ◽  
Author(s):  
Y. I. Arshavsky ◽  
T. G. Deliagina ◽  
G. N. Gamkrelidze ◽  
G. N. Orlovsky ◽  
Y. V. Panchin ◽  
...  
Keyword(s):  
Feeding Behavior ◽  
Motor Neurons ◽  
Gabab Receptors ◽  
Gamma Aminobutyric Acid ◽  
Rhythmic Movements ◽  
Rhythm Generator ◽  
Buccal Ganglia ◽  
Gabaa Receptor Antagonist ◽  
Feeding Rhythm ◽  
Clione Limacina

1. The pteropod mollusk Clione limacina is a predator, feeding on the small pteropod mollusk Limacina helicina. Injection of gamma-aminobutyric acid (GABA) into the hemocoel of the intact Clione evoked some essential elements of the hunting and feeding behavior, i.e., protracting the tentacles, opening the mouth, and triggering the rhythmic movements of the buccal mass. This pattern resembled that evoked by presentation of the prey: Clione grasped the Limacina by its tentacles, extracted the prey's body from the shell and then swallowed it. 2. In electrophysiological experiments, several targets of GABA action have been found: 1) direct application of GABA to isolated cerebral motor neurons projecting to the protractor muscles of tentacles resulted in their excitation; 2) GABA activated the feeding rhythm generator located in the buccal ganglia; 3) GABA exerted excitatory or inhibitory effects on the receptor cells of statocysts, the effects being mediated by the efferent input to these cells; 4) GABA suppressed the defense reaction, which is an inhibition of the locomotor activity and of tentacle motor neurons, arising in response to stimulation of the head afferents; and 5) GABA potentiated an excitatory action of the serotoninergic metacerebral cells on the feeding rhythm generator. 3. Effects of GABA on the tentacle motor neurons and the feeding rhythm generator are pharmacologically distinguishable. The action of GABA on the feeding rhythm generator was mimicked by baclofen (which activates the GABAB receptors in mammalian neurons) and was not sensitive to bicuculline (the GABAA receptor antagonist in mammals). On the other hand, bicuculline competitively inhibited the GABA-induced excitation of the tentacle motor neurons. 4. GABAergic neurons have been located in the cerebral, pedal, and buccal ganglia by means of immunohistochemical methods.

scholarly journals Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers

2019 ◽  
Vol 11 (4) ◽  
pp. 86 ◽  
Author(s):  
César Pérez López ◽  
María Delgado Rodríguez ◽  
Sonia de Lucas Santos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Income Tax ◽  
Fraud Detection ◽  
Personal Income ◽  
Personal Income Tax ◽  
Tax Returns ◽  
The Neural Networks ◽  
Tax Fraud ◽  
Efficiency Rate

The goal of the present research is to contribute to the detection of tax fraud concerning personal income tax returns (IRPF, in Spanish) filed in Spain, through the use of Machine Learning advanced predictive tools, by applying Multilayer Perceptron neural network (MLP) models. The possibilities springing from these techniques have been applied to a broad range of personal income return data supplied by the Institute of Fiscal Studies (IEF). The use of the neural networks enabled taxpayer segmentation as well as calculation of the probability concerning an individual taxpayer’s propensity to attempt to evade taxes. The results showed that the selected model has an efficiency rate of 84.3%, implying an improvement in relation to other models utilized in tax fraud detection. The proposal can be generalized to quantify an individual’s propensity to commit fraud with regards to other kinds of taxes. These models will support tax offices to help them arrive at the best decisions regarding action plans to combat tax fraud.

Neural networks trained with high-dimensional functions approximation data in high-dimensional space

2021 ◽  
pp. 1-12
Author(s):  
Jian Zheng ◽  
Jianfeng Wang ◽  
Yanping Chen ◽  
Shuping Chen ◽  
Jingjin Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Dimensional Space ◽  
Data Distribution ◽  
High Dimensional ◽  
Sufficient Information ◽  
Sufficient Data ◽  
High Dimensional Space ◽  
Positive Effects ◽  
The Neural Networks ◽  
Using Data

Neural networks can approximate data because of owning many compact non-linear layers. In high-dimensional space, due to the curse of dimensionality, data distribution becomes sparse, causing that it is difficulty to provide sufficient information. Hence, the task becomes even harder if neural networks approximate data in high-dimensional space. To address this issue, according to the Lipschitz condition, the two deviations, i.e., the deviation of the neural networks trained using high-dimensional functions, and the deviation of high-dimensional functions approximation data, are derived. This purpose of doing this is to improve the ability of approximation high-dimensional space using neural networks. Experimental results show that the neural networks trained using high-dimensional functions outperforms that of using data in the capability of approximation data in high-dimensional space. We find that the neural networks trained using high-dimensional functions more suitable for high-dimensional space than that of using data, so that there is no need to retain sufficient data for neural networks training. Our findings suggests that in high-dimensional space, by tuning hidden layers of neural networks, this is hard to have substantial positive effects on improving precision of approximation data.

Classification and Recognition of Plant Leaf Based on Neural Networks

2011 ◽  
Vol 464 ◽  
pp. 38-42 ◽  
Author(s):  
Ping Ye ◽  
Gui Rong Weng
Keyword(s):  
Neural Networks ◽  
Fractal Dimension ◽  
Recognition Rate ◽  
Image Binarization ◽  
Characteristic Parameters ◽  
Moment Invariant ◽  
Plant Leaf ◽  
The Neural Networks ◽  
Novel Method ◽  
The Moment

This paper proposed a novel method for leaf classification and recognition. In the method, the moment invariant and fractal dimension were regarded as the characteristic parameters of the plant leaf. In order to extract the representative characteristic parameters, pretreatment of the leaf images, including RGB-gray converting, image binarization and leafstalk removing. The extracted leaf characteristic parameters were further utilized as training sets to train the neural networks. The proposed method was proved effectively to reach a recognition rate about 92% for most of the testing leaf samples

scholarly journals Localization of Scattering Objects Using Neural Networks

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 11
Author(s):  
Domonkos Haffner ◽  
Ferenc Izsák
Keyword(s):  
Neural Networks ◽  
Plane Waves ◽  
Measurement Data ◽  
Scattered Wave ◽  
Training Data ◽  
Data Set ◽  
Main Compound ◽  
Incident Plane ◽  
The Neural Networks ◽  
Simulation Package

The localization of multiple scattering objects is performed while using scattered waves. An up-to-date approach: neural networks are used to estimate the corresponding locations. In the scattering phenomenon under investigation, we assume known incident plane waves, fully reflecting balls with known diameters and measurement data of the scattered wave on one fixed segment. The training data are constructed while using the simulation package μ-diff in Matlab. The structure of the neural networks, which are widely used for similar purposes, is further developed. A complex locally connected layer is the main compound of the proposed setup. With this and an appropriate preprocessing of the training data set, the number of parameters can be kept at a relatively low level. As a result, using a relatively large training data set, the unknown locations of the objects can be estimated effectively.

scholarly journals An Efficient Neural Network-Based Method for Diagnosing Faults of PV Array

2021 ◽  
Vol 13 (11) ◽  
pp. 6194
Author(s):  
Selma Tchoketch_Kebir ◽  
Nawal Cheggaga ◽  
Adrian Ilinca ◽  
Sabri Boulouma
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Modeling Process ◽  
Photovoltaic Arrays ◽  
Mode Of Operation ◽  
Different Types ◽  
The Neural Networks ◽  
Operational Modes ◽  
Types Of Faults ◽  
Electrical Data

This paper presents an efficient neural network-based method for fault diagnosis in photovoltaic arrays. The proposed method was elaborated on three main steps: the data-feeding step, the fault-modeling step, and the decision step. The first step consists of feeding the real meteorological and electrical data to the neural networks, namely solar irradiance, panel temperature, photovoltaic-current, and photovoltaic-voltage. The second step consists of modeling a healthy mode of operation and five additional faulty operational modes; the modeling process is carried out using two networks of artificial neural networks. From this step, six classes are obtained, where each class corresponds to a predefined model, namely, the faultless scenario and five faulty scenarios. The third step involves the diagnosis decision about the system’s state. Based on the results from the above step, two probabilistic neural networks will classify each generated data according to the six classes. The obtained results show that the developed method can effectively detect different types of faults and classify them. Besides, this method still achieves high performances even in the presence of noises. It provides a diagnosis even in the presence of data injected at reduced real-time, which proves its robustness.

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