Control of output pattern of a photorefractive oscillator using a spatially filtered feedback

2003 ◽  
Author(s):  
Hong Lin ◽  
Endri Trajani ◽  
Kristian M. Bodek ◽  
George A. Ruff
Keyword(s):  
Output Pattern

Higher order neurons in buccal ganglia of Pleurobranchaea elicit vomiting motor activity

1983 ◽  
Vol 50 (3) ◽  
pp. 658-670 ◽  
Author(s):  
A. D. McClellan
Keyword(s):  
High Frequency ◽  
Motor Pattern ◽  
Higher Order ◽  
Root Activity ◽  
Command Neurons ◽  
Sensory Inputs ◽  
Buccal Ganglia ◽  
Output Pattern ◽  
Stimulation Of

The buccal mass of the gastropod Pleurobranchaea is used during a regurgitation response that consists of a writhing phase interrupted by brief periodic bouts of a vomiting phase (17, 20). During transitions from writhing to vomiting, specific changes occur in the motor pattern (19, 20). Evidence is presented suggesting that at least some of the initiation or "command" neurons for vomiting reside in the buccal ganglia. The present paper examines the role of two candidate vomiting-initiation cells, the ventral white cells (VWC) and midganglionic cells (MC), in the buccal ganglia of isolated nervous systems. Stimulation of single VWCs activates a vomiting motor pattern, consisting in part of alternating buccal root activity. Furthermore, the VWCs fire in high-frequency bursts during episodes (i.e., bouts) of this same vomiting pattern. Mutual reexcitation between the VWCs and motor pattern generator (MPG) appears to produce the accelerated buildup and maintenance of vomiting rhythms. Brief stimulation of single MCs "triggers" bouts of a vomiting motor pattern, but the membrane potential of this cell is only modulated during this same pattern, at least in the isolated nervous system. It is proposed that in intact animals the MCs are activated by sensory inputs and briefly excite the VWC-MPG network, thereby turning on the mutual reexcitatory mechanism mentioned above and switching the output pattern. A general implication for gastropod research is that higher order neurons that activate buccal root activity cannot automatically be given the function of "feeding command neuron," as some cells clearly control other responses, such as vomiting.

Bioinspired Associative Memories

2011 ◽  
pp. 248-255
Author(s):  
Roberto A. Vazquez ◽  
Humberto Sossa
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Associative Memory ◽  
Vision System ◽  
Special Kind ◽  
Input Pattern ◽  
3D Object Recognition ◽  
Infant Vision ◽  
Output Pattern ◽  
Full Power

An associative memory AM is a special kind of neural network that allows recalling one output pattern given an input pattern as a key that might be altered by some kind of noise (additive, subtractive or mixed). Most of these models have several constraints that limit their applicability in complex problems such as face recognition (FR) and 3D object recognition (3DOR). Despite of the power of these approaches, they cannot reach their full power without applying new mechanisms based on current and future study of biological neural networks. In this direction, we would like to present a brief summary concerning a new associative model based on some neurobiological aspects of human brain. In addition, we would like to describe how this dynamic associative memory (DAM), combined with some aspects of infant vision system, could be applied to solve some of the most important problems of pattern recognition: FR and 3DOR.

Analysis and modeling of the multisegmental coordination of shortening behavior in the medicinal leech. I. Motor output pattern

1992 ◽  
Vol 68 (5) ◽  
pp. 1683-1692 ◽  
Author(s):  
G. Wittenberg ◽  
W. B. Kristan
Keyword(s):  
Motor Neurons ◽  
Longitudinal Muscle ◽  
Motor Pattern ◽  
Mechanical Stimulus ◽  
Medicinal Leech ◽  
Motor Output ◽  
Simultaneous Excitation ◽  
Ventral Longitudinal Muscle ◽  
Output Pattern ◽  
Simple Behavior

1. To understand how a multisegmental animal coordinates motor activity over more than one segment, we studied shortening behavior in the medicinal leech, in which several segments contract longitudinally in response to a moderately strong mechanical stimulus. 2. We first demonstrated that the neuronal activity responsible for shortening behavior occurred in semi-intact and isolated nerve cord preparations, and then characterized the responses of motor neurons in isolated preparations. The motor output during shortening was simultaneous excitation of motor neurons innervating dorsal longitudinal muscle and of motor neurons innervating ventral longitudinal muscle. 3. The stronger the stimulus, the more segments produced the shortening motor output, with the segments nearest the stimulus recruited first. 4. Although the shortening response was produced in several segments near the site of stimulation, it was never produced in the stimulated segment, where the local bending motor output pattern was produced. The motor pattern suggests that shortening, initially considered a very simple behavior, requires the involvement of at least few segmentally iterated interneurons.

scholarly journals Application of Reference Voltage Control Method of the Generator Using a Neural Network in Variable Speed Synchronous Generation System of DC Distribution for Ships

2020 ◽  
Vol 8 (10) ◽  
pp. 802
Author(s):  
Hyeonmin Jeon ◽  
Jongsu Kim
Keyword(s):  
Neural Network ◽  
Reference Voltage ◽  
Input Current ◽  
Variable Speed ◽  
Dc Distribution ◽  
The Neural Network ◽  
Output Pattern ◽  
Speed Change ◽  
Current Value ◽  
Speed Engine

In the case of DC power distribution-based variable speed engine synchronous generators, if the output reference voltage is kept constant regardless of the generator engine operating speed, it may cause damage to the internal device and windings of the generator due to over-flux or over-excitation. The purpose of this study is to adjust the generator reference voltage according to the engine speed change in the DC distribution system with the variable speed engine synchronous generator. A method of controlling the generator reference voltage according to the speed was applied by adjusting the value of the variable resistance input to the external terminal of the automatic voltage regulator using a neural network controller. The learning data of the neural network was measured through an experiment, and the input pattern was set as the rotational speed of the generator engine, and the output pattern was set as the input current of the potentiometer. Using the measured input/output pattern of the neural network, the error backpropagation learning algorithm was applied to derive the optimum connection weight to be applied to the controller. For the test, the variable speed operation range of the generator engine was set to 1100–1800 rpm, and the input current value of the potentiometer according to the speed increase or decrease within the operation range and the output of the voltage output from the actual generator were checked. As a result of neural network control, it was possible to confirm the result that the input current value of the potentiometer accurately reached the target value 4–20 mA at the point where the initial speed change occurred. It was confirmed that the reference voltage was also normally output in the target range of 250–440 V.

How to Incorporate New Pattern Pairs without Having to Teach the Previously Acquired Pattern Pairs

1992 ◽  
Vol 4 (6) ◽  
pp. 880-887 ◽  
Author(s):  
H. M. Wabgaonkar ◽  
A. R. Stubberud
Keyword(s):  
Hilbert Space ◽  
Associative Memory ◽  
Reproducing Kernel ◽  
Reproducing Kernel Hilbert Space ◽  
Input Output ◽  
Space Setting ◽  
Output Pattern ◽  
Orthogonalization Procedure ◽  
Memory Synthesis

In this paper, we deal with the problem of associative memory synthesis. The particular issue that we wish to explore is the ability to store new input-output pattern pairs without having to modify the path weights corresponding to the already taught pattern pairs. The approach to the solution of this problem is via interpolation carried out in a Reproducing Kernel Hilbert Space setting. An orthogonalization procedure carried out on a properly chosen set of functions leads to the solution of the problem.

Neural Network Modeling for Monitoring Petroleum Pipelines

2016 ◽  
Vol 26 ◽  
pp. 122-131 ◽  
Author(s):  
Ighodaro Osarobo ◽  
Akaeze Chika
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Network Modeling ◽  
Petroleum Products ◽  
Input Pattern ◽  
Neural Network Modeling ◽  
Diagnostic Problem ◽  
Common Occurrence ◽  
Output Pattern ◽  
Pipeline Failures

It is common occurrence that the transportation of petroleum products via pipelines is susceptible to failure either naturally or intentionally. The paper is a diagnostic problem having continuous inputs of pattern recognition used in predicting pipeline failures. Our problem is to design a neural network that will recognize failure events in pipelines when fed with an input pattern denoting such a scenario. A neural network paradigm is selected, and encoding of input is done to obtain the input pattern. The selected model is simulated and trained to recognize the output pattern, which in our scenario after training, goes into operational mode.The neural network is fully implemented on a Pentium II MMX computer with a Borland C++ builder.

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