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NIPS
2004
13 years 11 months ago
Reducing Spike Train Variability: A Computational Theory Of Spike-Timing Dependent Plasticity
Experimental studies have observed synaptic potentiation when a presynaptic neuron fires shortly before a postsynaptic neuron, and synaptic depression when the presynaptic neuron ...
Sander M. Bohte, Michael C. Mozer
UAI
2004
13 years 11 months ago
Dependent Dirichlet Priors and Optimal Linear Estimators for Belief Net Parameters
A Bayesian belief network is a model of a joint distribution over a finite set of variables, with a DAG structure representing immediate dependencies among the variables. For each...
Peter Hooper
WSCG
2004
143views more  WSCG 2004»
13 years 11 months ago
View Dependent Stochastic Sampling for Efficient Rendering of Point Sampled Surfaces
In this paper we present a new technique for rendering very large datasets representing point-sampled surfaces. Rendering efficiency is considerably improved by using stochastic s...
Sushil Bhakar, Liang Luo, Sudhir P. Mudur
NIPS
2003
13 years 11 months ago
Analytical Solution of Spike-timing Dependent Plasticity Based on Synaptic Biophysics
Spike timing plasticity (STDP) is a special form of synaptic plasticity where the relative timing of post- and presynaptic activity determines the change of the synaptic weight. O...
Bernd Porr, Ausra Saudargiene, Florentin Wörg...
WSC
1997
13 years 11 months ago
Modeling Dependencies in Stochastic Simulation Inputs
We discuss some basic techniques for modeling dependence between the random variables that are inputs to a simulation model, with the main emphasis being continuous bivariate dist...
James R. Wilson