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BMCBI
2008
77views more  BMCBI 2008»
13 years 8 months ago
Stochastic models for the in silico simulation of synaptic processes
Background: Research in life sciences is benefiting from a large availability of formal description techniques and analysis methodologies. These allow both the phenomena investiga...
Andrea Bracciali, Marcello Brunelli, Enrico Catald...
NIPS
1997
13 years 9 months ago
Learning Generative Models with the Up-Propagation Algorithm
Up-propagation is an algorithm for inverting and learning neural network generative models. Sensory input is processed by inverting a model that generates patterns from hidden var...
Jong-Hoon Oh, H. Sebastian Seung
BMCBI
2006
141views more  BMCBI 2006»
13 years 8 months ago
Visual setup of logical models of signaling and regulatory networks with ProMoT
Background: The analysis of biochemical networks using a logical (Boolean) description is an important approach in Systems Biology. Recently, new methods have been proposed to ana...
Julio Saez-Rodriguez, Sebastian Mirschel, Rebecca ...
TCS
2008
13 years 8 months ago
GemCell: A generic platform for modeling multi-cellular biological systems
The mass and complexity of biological information requires computer-aided simulation and analysis to help scientists achieve understanding and guide experimentation. Although livi...
Hila Amir-Kroll, Avital Sadot, Irun R. Cohen, Davi...
CMSB
2007
Springer
14 years 2 months ago
Expressive Models for Synaptic Plasticity
We explore some presynaptic mechanisms of the calyx of Held synapse through a stochastic model. The model, drawn from a kinetic approach developed in literature, exploits process c...
Andrea Bracciali, Marcello Brunelli, Enrico Catald...