On mathematical analysis of Pavlovian conditioning learning process using artificial neural network model

Hasan, Habsah; Watany, m

doi:10.1109/melcon.2000.879999

Cited by 22 publications

(27 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These intensities observed to be in proportionality with the increase of the subsequent number of trials. So, this relation agrees with odd sigmoid function curve as reaching saturation state [3] [11] Referring to behaviorism learning theory presented at [20] ,Thorndike had suggested three principles, which instructors (who adopted teaching based on behaviorism learning theory) should apply in order to promote effectiveness of behavioral learning process. These principles are given as follows:  Present the information to be learned in small behaviorally defined steps 1945  Give rapid feedback to pupils regarding the accuracy of their learning.…”

Section: E Revising Of Pavlov's Work [10]supporting

confidence: 61%

“…It is clear that this process performed similarly as ACS searching for minimum tour when solving TSP [1]. Furthermore, it obeys the learning performance observed during psycho experimental work carried for animal learning [3]. Figure 16:-Idealized learning curve of the LMS algorithm adapted from [17].…”

Section: Least Mean Square Lms Algorithmmentioning

confidence: 58%

“…That concerned with observed resulting errors, time responses, learning rate values, gain factor values versus number of trials, training dataset vectors intercommunication among ants and number of neurons as basic processing elements [5][13] [14]. However, it seems to observe diversity of behavioral learning curves performance (till reaching optimum state) for proposed biological systems, both are similar to each other (considering normalization of performance curves) [3] [6]. Interestingly, behavioral intelligence & learning performance phenomena carried out by both nonhuman biological systems are characterized by their adaptive behavioral responses to their living environmental conditions.…”

Section: B Second Algorithmic Learning Approachmentioning

confidence: 99%

See 2 more Smart Citations

On Comparative Study of Neural Networks' Application for Three Behavioral Learning Systems' Activities Versus Swarm Intelligence Performance.

Mustafa¹,

Tourkia.²

2017

IJAR

View full text Add to dashboard Cite

Artificial neural network modeling; Swarm Intelligence; ant colony system; traveling salesman problem; computational biology. This piece of research addresses an interesting comparative analytical study. Which considers two concepts of diverse algorithmic computational intelligence approaches related tightly with Neural and Non-Neural Systems. The first algorithmic intelligent approach concerned with observed obtained practical results after three neural animal systems' activities. Namely, they are Pavlov's, and Thorndike's experimental work. Besides a mouse's trial during its movement inside figure of eight (8) maze, to reach an optimal solution for reconstruction problem. Conversely, second considers algorithmic Swarm Intelligent (SI) approach originated from resulting activities of Non-Neural Ant Colony System (ACS). These results obtained after reaching an optimal solution while solving Traveling Sales-man Problem (TSP). Interestingly, the effect of the increase for agents' number (either neurons or ants) on learning systems' performance shown to be in agreement of each other for both (neural and non-neural) systems. Finally, performance of both intelligent learning paradigms shown to be in agreement with learning convergence process searching for Least Mean Square LMS error algorithm. That's during application of training phase of three Artificial Neural Network (ANN) models. Accordingly, adopted ANN modeling is a relevant and realistic tool to investigate observations and analyze performance for both selected computational intelligence (biological behavioral learning) systems.

show abstract

Section: E Revising Of Pavlov's Work [10]supporting

confidence: 61%

Section: Least Mean Square Lms Algorithmmentioning

confidence: 58%

Section: B Second Algorithmic Learning Approachmentioning

confidence: 99%

See 1 more Smart Citation

On Comparative Study of Neural Networks' Application for Three Behavioral Learning Systems' Activities Versus Swarm Intelligence Performance.

Mustafa¹,

Tourkia.²

2017

IJAR

View full text Add to dashboard Cite

show abstract

“…As the children perform, thir reading tasks based on previously learned lessons former experienced acquired knowledge. Furthermore, the simulated reading process flows that model is analogously straightforward to the Pavlovian conditioning learning [27]. The input stimuli to the model considered either conditioned or unconditioned stimuli.…”

Section: A Tabulated Resultsmentioning

confidence: 99%

Neural Networks' Application for Comparative Study of Academic Performance Considering Two Diversified Instructional Methodologies

2017

IJAMAE

View full text Add to dashboard Cite

Abstract-This paper presents a realistic and fairly interdisciplinary approach to perform comparative analysis and evaluation of academic performance of two applicable educational packages (modules). Both have considered as diversified instructional methodologies with effective utilization of visual and auditory multisensory (multimedia) teaching for two different topics taught to children with average age about 11 years old. By more details, one of these topics concerned with learning "How to solve long division problem?‖. That has carried out by sequential processes: Divide, Multiply, Subtract, Bring Down, and repeat (if necessary). However, the second is associated with the critical instructional issue of "how to teach/learn reading?" Both adopted modules have carefully designed in order to develop two multimedia educational packages to be applicable for both above adopted topics at children's classrooms. Interestingly, obtained results proved to have significant improved distinction for the applied multisensory methodology (experimental groups) versus the classical one (control groups). That improvement has been clearly observed not only regarding average (mean) values of academic achievement, but also considering coefficient of variation which improvement results in more consistency of the statistical distribution of academic achievement's values. Furthermore, both multisensory designed modules proved to be in well agreement likewise the Artificial Neural Network (ANN) associative memories theory, cognitive multimedia, and classical conditioning.

show abstract

“…The presented article belongs to general topics, that link neurobiology and information processing sciences. Consequently, this work introduced following after obtained results from some other bio-experimental research work, and corresponding computational and/or mathematical models [9], [10], [11], [12].So, this research work article results in capturing of some essential principles of natural biological information processing [13]. More specifically, this paper considers mainly, learning convergence processing observed in two types of natural biological systems.…”

Section: Introductionmentioning

confidence: 99%

On Principles 0f Biological Information Processing Concerned with Learning Convergence Mechanism in Neural and Non-Neural Bio-Systems

Hassan

International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on In

View full text Add to dashboard Cite

This article presents an investigational overview for principles of biological information processing. That is fulfilled through analysis of some learning case examples for both neural and non-neural biological systems. Mainly, this work concerned with learning performance curves observed after some experimental work and mathematical simulation programming. Three learning case examples are considered, two of them belong to neural biosystems. Both are associated to results of some animals psych-learning experiments .Third case example belongs to non-neural bio-systems. That is ant colony system (ACS) leaning how to solve optimally minimum path problem to survive (food storage process). Mathematical formulation of (ACS) behavior is presented after computational biology of optimum solution for traveling sales-man problem. The deduced formula shown to be very similar to sigmoid activation function. That is used commonly in artificial neural network ANNs models. Biological information processing for all examples (and more other animal learning work) shown to obey commonly least mean square (LMS) error algorithm with variations of learning rate parameter. Finally, some interesting conclusive remarks, and interesting analogous features (for learning mechanisms) among examples are presented.

show abstract

On mathematical analysis of Pavlovian conditioning learning process using artificial neural network model

Cited by 22 publications

References 4 publications

On Comparative Study of Neural Networks' Application for Three Behavioral Learning Systems' Activities Versus Swarm Intelligence Performance.

On Comparative Study of Neural Networks' Application for Three Behavioral Learning Systems' Activities Versus Swarm Intelligence Performance.

Neural Networks' Application for Comparative Study of Academic Performance Considering Two Diversified Instructional Methodologies

On Principles 0f Biological Information Processing Concerned with Learning Convergence Mechanism in Neural and Non-Neural Bio-Systems

Contact Info

Product

Resources

About