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» Inferring Elapsed Time from Stochastic Neural Processes
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NIPS
2003
13 years 6 months ago
Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model
Recent work has examined the estimation of models of stimulus-driven neural activity in which some linear filtering process is followed by a nonlinear, probabilistic spiking stag...
Jonathan Pillow, Liam Paninski, Eero P. Simoncelli
BMCBI
2008
77views more  BMCBI 2008»
13 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...
PAMI
2010
113views more  PAMI 2010»
13 years 3 months ago
Hierarchical Bayesian Modeling of Topics in Time-Stamped Documents
—We consider the problem of inferring and modeling topics in a sequence of documents with known publication dates. The documents at a given time are each characterized by a topic...
Iulian Pruteanu-Malinici, Lu Ren, John William Pai...
IJON
2007
91views more  IJON 2007»
13 years 5 months ago
Dynamics of parameters of neurophysiological models from phenomenological EEG modeling
We investigate a recently proposed method for the analysis of oscillatory patterns in EEG data, with respect to its capacity of further quantifying processes on slower (< 1 Hz)...
E. Olbrich, Thomas Wennekers
ICML
2009
IEEE
14 years 6 months ago
A stochastic memoizer for sequence data
We propose an unbounded-depth, hierarchical, Bayesian nonparametric model for discrete sequence data. This model can be estimated from a single training sequence, yet shares stati...
Frank Wood, Cédric Archambeau, Jan Gasthaus...