This paper, Neuronal calculation. Theory and economic applications, is designed to support all those who want to know more or less analytical matters on micro and macro economic science, from the perspective of neural calculation.
The first chapter is a separate one, Preliminaries and fundamental concepts in neuronal calculation, where an incursion into basic issues of neural computation is made, defining and characterizing artificial intelligence, drawing a parallel between biological and artificial neuron, showing types of neural network, classification of neural networks and last but not least making an overview of areas of application of neural networks.
Chapters 2-6 present prior to signal propagation neural networks (feed-forward), neural networks based on radial activation functions (RNA-RBF), self-organized neural networks (with unsupervised learning and clustering algorithms), recurrent networks (feedback) and also associative memories. Throughout the entire paper the theoretical elements with the applied ones coexist.
In Chapter 7, dedicated to the proposed applications, one can find a total of 68 applications which closely follow the issues presented in the first 6 chapters.
The paper is completed, also, by graphics, economic interpretations, all of them helping to understand and to clarify the presented theoretical concepts.
Consequently, the paper Neuronal calculation. Theory and economic applications is recommended both to students which follow Economic modeling and neural calculation and also to those that follow courses containing neural calculation component and researchers and those interested in various aspects related to this field.
For those interested in learning about other sides of neural calculation, the presented bibliography is a good point to start.