Synthesis and Structure−Activity Relationships of a New Model of Arylpiperazines. 6. Study of the 5-HT1A/α1-Adrenergic Receptor Affinity by Classical Hansch Analysis, Artificial Neural Networks, and Computational Simulation of Ligand Recognition†

2001 ◽  
Vol 44 (2) ◽  
pp. 198-207 ◽  
Author(s):  
María L. López-Rodríguez ◽  
M. José Morcillo ◽  
Esther Fernández ◽  
M. Luisa Rosado ◽  
Leonardo Pardo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Adrenergic Receptor ◽  
Computational Simulation ◽  
Ligand Recognition ◽  
Receptor Affinity ◽  
New Model ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Hansch Analysis

An Approach to Artificial Concept Learning Based on Human Concept Learning by Using Artificial Neural Networks

2011 ◽  
pp. 130-145
Author(s):  
Enrique Mérida-Casermeiro ◽  
Domingo López-Rodríguez ◽  
J.M. Ortiz-de-Lazcano-Lobato
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Associative Memory ◽  
Concept Learning ◽  
Energy Function ◽  
Hebbian Learning ◽  
Clustering Algorithms ◽  
Network Capacity ◽  
New Model

In this chapter, two important issues concerning associative memory by neural networks are studied: a new model of hebbian learning, as well as the effect of the network capacity when retrieving patterns and performing clustering tasks. Particularly, an explanation of the energy function when the capacity is exceeded: the limitation in pattern storage implies that similar patterns are going to be identified by the network, therefore forming different clusters. This ability can be translated as an unsupervised learning of pattern clusters, with one major advantage over most clustering algorithms: the number of data classes is automatically learned, as confirmed by the experiments. Two methods to reinforce learning are proposed to improve the quality of the clustering, by enhancing the learning of patterns relationships. As a related issue, a study on the net capacity, depending on the number of neurons and possible outputs, is presented, and some interesting conclusions are commented.

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