Compensatory Hebbian learning for categorisation in simulated biological neural nets

2013 ◽  
Vol 6 ◽  
pp. 3-7 ◽  
Author(s):  
Christian R. Huyck ◽  
Ian G. Mitchell
Keyword(s):  
Hebbian Learning ◽  
Neural Nets ◽  
Biological Neural Nets

NEURAL NETS FOR DUAL SUBSPACE PATTERN RECOGNITION METHOD

1991 ◽  
Vol 02 (03) ◽  
pp. 169-184 ◽  
Author(s):  
Lei Xu ◽  
Adam Krzyzak ◽  
Erkki Oja
Keyword(s):  
Pattern Recognition ◽  
Hebbian Learning ◽  
Network Models ◽  
Neural Nets ◽  
Experimental Comparison ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Neural Network Models ◽  
Learning Rules ◽  
Dual Subspace

A new modification of the subspace pattern recognition method, called the dual subspace pattern recognition (DSPR) method, is proposed, and neural network models combining both constrained Hebbian and anti-Hebbian learning rules are developed for implementing the DSPR method. An experimental comparison is made by using our model and a three-layer forward net with backpropagation learning. The results illustrate that our model can outperform the backpropagation model in suitable applications.

Hebbian learning reconsidered: Representation of static and dynamic objects in associative neural nets

1989 ◽  
Vol 60 (6) ◽  
pp. 457-467 ◽  
Author(s):  
A. Herz ◽  
B. Sulzer ◽  
R. Kühn ◽  
J. L. van Hemmen
Keyword(s):  
Hebbian Learning ◽  
Neural Nets ◽  
Dynamic Objects

scholarly journals Hawkes processes with variable length memory and an infinite number of components

2017 ◽  
Vol 49 (1) ◽  
pp. 84-107 ◽  
Author(s):  
Pierre Hodara ◽  
Eva Löcherbach
Keyword(s):  
Infinite Number ◽  
Variable Length ◽  
Neural Nets ◽  
Interaction Graph ◽  
Hawkes Processes ◽  
Number Of Components ◽  
Stationary Version ◽  
Biological Neural Nets ◽  
Saturation Threshold ◽  
Type Decomposition

Abstract In this paper we propose a model for biological neural nets where the activity of the network is described by Hawkes processes having a variable length memory. The particularity in this paper is that we deal with an infinite number of components. We propose a graphical construction of the process and build, by means of a perfect simulation algorithm, a stationary version of the process. To implement this algorithm, we make use of a Kalikow-type decomposition technique. Two models are described in this paper. In the first model, we associate to each edge of the interaction graph a saturation threshold that controls the influence of a neuron on another. In the second model, we impose a structure on the interaction graph leading to a cascade of spike trains. Such structures, where neurons are divided into layers, can be found in the retina.

Advanced Statistical Tools for Improving Yield and Reliability

1999 ◽  
Author(s):  
Richard C. Kittler
Keyword(s):  
Data Mining ◽  
Analysis Data ◽  
Neural Nets ◽  
Future Trends ◽  
Statistical Tools ◽  
Mining Technology ◽  
Explanatory Variables ◽  
Data Coverage ◽  
Statistical Approaches ◽  
Adequate Data

Abstract Analysis of manufacturing data as a tool for failure analysts often meets with roadblocks due to the complex non-linear behaviors of the relationships between failure rates and explanatory variables drawn from process history. The current work describes how the use of a comprehensive engineering database and data mining technology over-comes some of these difficulties and enables new classes of problems to be solved. The characteristics of the database design necessary for adequate data coverage and unit traceability are discussed. Data mining technology is explained and contrasted with traditional statistical approaches as well as those of expert systems, neural nets, and signature analysis. Data mining is applied to a number of common problem scenarios. Finally, future trends in data mining technology relevant to failure analysis are discussed.

Sign in / Sign up

Export Citation Format

Share Document