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NIPS
2000
13 years 9 months ago
Processing of Time Series by Neural Circuits with Biologically Realistic Synaptic Dynamics
Experimental data show that biological synapses behave quite differently from the symbolic synapses in common artificial neural network models. Biological synapses are dynamic, i....
Thomas Natschläger, Wolfgang Maass, Eduardo D...
BIOINFORMATICS
2006
106views more  BIOINFORMATICS 2006»
13 years 8 months ago
Identification of biochemical networks by S-tree based genetic programming
Motivation: Most previous approaches to model biochemical networks havefocusedeither on the characterization of a networkstructurewith a number of components or on the estimation ...
Dong-Yeon Cho, Kwang-Hyun Cho, Byoung-Tak Zhang
APIN
1998
139views more  APIN 1998»
13 years 8 months ago
Evolutionary Learning of Modular Neural Networks with Genetic Programming
Evolutionary design of neural networks has shown a great potential as a powerful optimization tool. However, most evolutionary neural networks have not taken advantage of the fact ...
Sung-Bae Cho, Katsunori Shimohara
BIODATAMINING
2008
163views more  BIODATAMINING 2008»
13 years 8 months ago
Multivariate profiling of neurodegeneration-associated changes in a subcellular compartment of neurons via image processing
Background: Dysfunction in the endolysosome, a late endosomal to lysosomal degradative intracellular compartment, is an early hallmark of some neurodegenerative diseases, in parti...
Saravana K. Kumarasamy, Yunshi Wang, Vignesh Viswa...
CODES
2003
IEEE
14 years 1 months ago
Deriving process networks from weakly dynamic applications in system-level design
We present an approach to the automatic derivation of executable Process Network specifications from Weakly Dynamic Applications. We introduce the notions of Dynamic Single Assig...
Todor Stefanov, Ed F. Deprettere