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BMCBI
2008
77views more  BMCBI 2008»
15 years 5 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
15 years 7 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»
15 years 5 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
15 years 5 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
15 years 11 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...