Input Pattern Complexity Determines Specialist and Generalist Populations in Drosophila Neural Network

2018 ◽  
pp. 296-303
Author(s):  
Aaron Montero ◽  
Jessica Lopez-Hazas ◽  
Francisco B. Rodriguez
Keyword(s):  
Neural Network ◽  
Input Pattern ◽  
Pattern Complexity

MAGNOCELLULAR PATHWAY FOR ROTATION INVARIANT NEOCOGNITRON

1993 ◽  
Vol 04 (01) ◽  
pp. 43-54 ◽  
Author(s):  
CHRISTOPHER HIAN-ANN TING
Keyword(s):  
Neural Network ◽  
Visual System ◽  
Neural Network Model ◽  
Visual Information ◽  
Input Pattern ◽  
Arbitrary Orientation ◽  
Magnocellular Pathway ◽  
Rotation Invariant ◽  
Parvocellular Pathway ◽  
Simulation Programme

In the mammalian visual system, magnocellular pathway and parvocellular pathway cooperatively process visual information in parallel. The magnocellular pathway is more global and less particular about the details while the parvocellular pathway recognizes objects based on the local features. In many aspects, Neocognitron may be regarded as the artificial analogue of the parvocellular pathway. It is interesting then to model the magnocellular pathway. In order to achieve "rotation invariance" for Neocognitron, we propose a neural network model after the magnocellular pathway and expand its roles to include surmising the orientation of the input pattern prior to recognition. With the incorporation of the magnocellular pathway, a basic shift in the original paradigm has taken place. A pattern is now said to be recognized when and only when one of the winners of the magnocellular pathway is validified by the parvocellular pathway. We have implemented the magnocellular pathway coupled with Neocognitron parallel on transputers; our simulation programme is now able to recognize numerals in arbitrary orientation.

DESIGN OF A VLSI HAMMING NEURAL NETWORK FOR ARRHYTHMIA CLASSIFICATION

2009 ◽  
Vol 18 (04) ◽  
pp. 825-839 ◽  
Author(s):  
BEHZAD GHANAVTI ◽  
GHOLAMREZA SHOMALNASAB
Keyword(s):  
Neural Network ◽  
Cardiac Arrhythmia ◽  
Classification System ◽  
Cmos Technology ◽  
Input Pattern ◽  
Implantable Cardioverter Defibrillators ◽  
Binary Input ◽  
Cardioverter Defibrillators

The implantable cardioverter defibrillators (ICDs) detect and treat dangerous cardiac arrhythmia. This paper describes a VLSI neural network chip to be implemented using 0.35 μ CMOS technology which acts as an intercardia tachycardia classification system. The Hamming network used to classify non binary input pattern and also reduce impact of noise, drift and offset inherent in analog application. Simulation result using HSPICE and level 49 parameters (BSIM3V3) that verify the functionality of circuit are presented.

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.

A Dual Neural Network for Kinematic Control of Redundant Manipulators Using Input Pattern Switching

2010 ◽  
Vol 63 (1) ◽  
pp. 101-113 ◽  
Author(s):  
Ahmad Reza Khoogar ◽  
Alireza K. Tehrani ◽  
Mehdi Tajdari
Keyword(s):  
Neural Network ◽  
Input Pattern ◽  
Redundant Manipulators ◽  
Kinematic Control

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.

Hierarchical Function Approximation with a Neural Network Model

2012 ◽  
pp. 466-479
Author(s):  
Luis F. de Mingo ◽  
Nuria Gómez ◽  
Fernando Arroyo ◽  
Juan Castellanos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Polynomial Equation ◽  
Neural System ◽  
Input Pattern ◽  
Activation Functions ◽  
Proposed Model ◽  
Generate Polynomial

This article presents a neural network model that permits to build a conceptual hierarchy to approximate functions over a given interval. Bio-inspired axo-axonic connections are used. In these connections the signal weight between two neurons is computed by the output of other neuron. Such arquitecture can generate polynomial expressions with lineal activation functions. This network can approximate any pattern set with a polynomial equation. This neural system classifies an input pattern as an element belonging to a category that the system has, until an exhaustive classification is obtained. The proposed model is not a hierarchy of neural networks, it establishes relationships among all the different neural networks in order to propagate the activation. Each neural network is in charge of the input pattern recognition to any prototyped category, and also in charge of transmitting the activation to other neural networks to be able to continue with the approximation.

scholarly journals Jaringan Syaraf Tiruan Perceptron untuk Pengenalan Pola Makanan Sehat Rendah Kolesterol

2018 ◽  
Author(s):  
Muhammad Khoiruddin Harahap ◽  
Nurul Khairina
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Human Brain ◽  
Input Pattern ◽  
Cholesterol Diet ◽  
Weight Changes ◽  
High Cholesterol ◽  
Cholesterol Levels ◽  
Human Nerve ◽  
Low Cholesterol

With the computer, the computing process has become easier. Computers are used to model the biological nerves of the human brain, computers are trained and are taught how to act as human nerve cells capable of recognizing simple patterns. The artificial neural network is able to recognize the input pattern and will issue output in accordance with the target to be achieved. In this research Perceptron method is used to recognize the input pattern in the form of healthy food low cholesterol. This method works by adjusting the input with the target and doing the weight changes until there are no more errors found in each epoch. The result of the introduction of low cholesterol diet was found that egg whites, freshwater fish, and cheese were foods that contained low to moderate cholesterol levels. However, quail eggs are foods that contain high cholesterol levels..

scholarly journals Performance Evaluation of Pattern Storage Network of Associative memory with Sub-optimal GA for Hand written Hindi SWARS

2013 ◽  
Vol 3 (1) ◽  
pp. 83-93
Author(s):  
Rajesh Lavania ◽  
Manu Pratap Singh
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Associative Memory ◽  
Hopfield Neural Network ◽  
Input Pattern ◽  
Weight Matrix ◽  
Initial Population ◽  
Optimal Form ◽  
Optimal Weight ◽  
Rate Of Success

In this paper we are performing the evaluation of Hopfield neural network as Associative memory for recalling of memorized patterns from the Sub-optimal genetic algorithm for Handwritten of Hindi language. In this process the genetic algorithm is employed from sub-optimal form for recalling of memorized patterns corresponding to the presented noisy prototype input patterns. The sub-optimal form of GA is considered as the non-random initial population or solution. So, rather than random start, the GA explores from the sum of correlated weight matrices for the input patterns of training set. The objective of this study is to determine the optimal weight matrix for correct recalling corresponds to approximate prototype input pattern of Hindi ‘SW. In this study the performance of neural network is evaluated in terms of the rate of success for recalling of memorized Hindi  for presented approximate prototype input pattern with GA in two aspects. The first aspect reflects the random nature of the GA and the second one exhibit the suboptimal nature of the GA for its exploration.The simulated results demonstrate the better performance of network for recalling of the memorized Hindi SWARS using genetic algorithm to evolve the population of weights from sub-optimal weight matrix. 

Pattern Learning and the Control of Behaviour by All-Inhibitory Neural Network Hierarchies

Perception ◽  
1975 ◽  
Vol 4 (1) ◽  
pp. 35-50 ◽  
Author(s):  
Vernon Dobson
Keyword(s):  
Neural Network ◽  
Reaction Times ◽  
Input Pattern ◽  
Pattern Learning ◽  
Growth Processes ◽  
Learning Capacity ◽  
Inhibitory Network ◽  
Complex Sequences ◽  
Random Growth

An all-inhibitory network which learns by selective disconnection of synapses is described. This is similar to an ‘associative net'; however, it is simpler in that its neurons do not need to perform arithmetical operations, and the net does not require additional threshold modulating neurons in order to cope with input patterns which are incomplete, or of differing sizes. This fundamental simplicity permits a greater variety and density of connections. These can multiply the capacity of the nets to learn complex sequences of patterns without being saturated. An “all-connected‘’ net is described which has the holograph-like capacity to reconstruct the whole of an input pattern from part patterns without involving delays or threshold devices. All of these inhibitory nets can construct themselves by means of simple random growth processes, without incurring any loss of learning capacity of holographic properties. Similarly, synapses can be allowed to potentiate with use, so that reaction times are progressively reduced by practice, without any reduction in the quality of the performance. Inhibitory connections between arrays can give patterns in one array control over the allocation of channels in which lower arrays store learned information. A description is given of a model, decentralised, inhibitory hierarchy consisting of inter-connected arrays which can learn to execute goal-directed TOTE-type programs of behaviour by means of a simple ‘putting-through’ procedure.

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