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As a fundamental aspect in many ar tificial neural networ ks cour ses, the development of pr ogr amming skills is a fundamental goal. This paper has two objectives: the first objective of th is pa per is in t r oducing the R langua ge for stat ist ica l compu ting a s a power ful tool for exemplify algor ithms, for executing numer ical computations, and fo...

The extent to which epistasis affects the genetic architecture of complex traits is difficult to quantify, and identifying variants in natural populations with epistatic interactions is challenging. Previous studies in Drosophila implicated extensive epistasis between variants in genes that affect neural connectivity and contribute to natural variation in olfactory response to benzaldehyde. In ...

Abstrac t . We propose a new hyb rid genetic back propagation training algorithm based on a unique functional matching recombination method. T he method is used to evolve pop ulations of neur al networks and provides versatility in network architecture and activation functions. Net reorganization and reconstruction is carried out prior to genet ic recomb ina tion usin g a funct ional behavior c...

Editor's Note: These short, critical reviews of recent papers in the Journal, written exclusively by graduate students or postdoctoral fellows, are intended to summarize the important findings of the paper and provide additional insight and commentary. For more information on the format and purpose of the Journal Club, please see Review of Geerligs et al. The study of functional connectivity (F...